Tag: Focus

Mid-Semester Course Corrections: Using the MSF Model to Engage Students and Improve Courses – Faculty Focus
In general, educators recognize the value of student feedback: it can help us better understand the classroom experience, modify learning activities, or adjust our policies and practices to improve student outcomes. Unfortunately, the most common form of feedback that instructors receive, the mandatory end-of-semester evaluation, is primarily intended to assess faculty rather than the teaching and learning experience. It rarely asks questions focused on student learning. Moreover, it takes place at a point in time that provides no opportunity to make the type of changes that might benefit the students who complete it (George, 2017; Holton, et al., 2016; Kahn, 1993; Medina, 2011; Nilson, 2016).

In contrast, the Midterm Student Feedback (MSF) mechanism, which is generally voluntary, is fundamentally focused on teaching and learning. The MSF asks students simple, direct questions focused on the teaching and learning process in their current course. Put more simply, it asks students to discuss what is and isn’t helping them learn (Dangel & Lindsey, 2014; Nelms, 2015; Payette & Brown, 2018; Ufland, 2020; Veeck, et al., 2016). The students’ feedback is then analyzed and used by the instructor to make any changes that he or she believes will help the students improve their learning outcomes over the course of the rest of the semester (Gooblar, 2017; Harris & Stevens, 2013; McGrath, 2014; Veeck, et al., 2016).

How It Works

The are several different forms of MSF that can be employed to attain feedback on the student learning process. For the purposes of clarity and simplicity, we feel that two are worth discussing here: the Small Group Instructional Diagnosis (SGID) and Bare Bones Questions (BBQ). While these methods vary in terms of the time and resources required to conduct them, all MSFs share certain key elements:
1. They must be conducted at a point in the semester when students have had enough time to become familiar with the course pedagogy, yet still early enough for meaningful changes to be made;
1. They must be a voluntary effort to improve learning, as opposed to an evaluation mandated by the institution;
1. They must be anonymous;
1. They must be conducted, analyzed, conveyed to the instructor, and discussed with the students in a timely fashion, usually within a week (Ufland, 2020).
Small Group Instructional Diagnosis (SGID)

The SGID is a collaborative form of MSF that requires the cooperation of a facilitator, typically a teaching and learning specialist or a trusted colleague. The process requires about thirty minutes of class time, so it is fairly efficient.

Before the evaluation, the facilitator and the instructor identify three or four open-ended questions to structure the MSF. The questions should encourage students to focus on the learning experience, not on the instructor. We’ve found the following questions effective: 
1. What in this course has helped to improve your learning? 
1. What in this course has hindered your learning? 
1. Is there anything you would like the instructor to change? 
On the day of the MSF, the instructor should introduce the facilitator and encourage students to be honest in their feedback, then leave the room. The facilitator then provides an overview of the process, divides students into groups of five to eight students, and asks them to discuss the three questions. 

Once students have worked through the questions together, the facilitator reconvenes the full group and elicits responses. The facilitator records all the responses for the class but also takes note of those points that seem to represent a consensus. The facilitator asks follow-up questions as needed to understand the context in which the responses are being offered. If the suggested questions above are used, the third can serve to synthesize responses. 

After the session, the facilitator writes a brief report synthesizing feedback from the class. The facilitator can use their judgment here: not all feedback needs to be shared, since the goal is to provide contextualized, actionable feedback. One way to achieve this more easily is to focus on those views that emerged as consensus points across the class. Indeed, one of the benefits of the MSF is that unhelpful feedback can be filtered out before the instructor sees it (Ufland, 2020). The facilitator may also provide a short, immediate “act now” list of items to the instructor and provide a more detailed report subsequently. This can enable the instructor to have an immediate discussion of the feedback with students, which is critical to the success of the model (Weimer, 2016).

Bare Bones Questions (BBQ)

The BBQ is a highly simplified form of midterm feedback that requires considerably less time than the more formal SGID. It also may be conducted without the assistance of a facilitator and can be effective in very small classes.

In this mode, the instructor provides written or digital copies of the questions – we suggest those listed above – to students near the end of a class session. The students then meet in small groups, compile their own responses, and elect one student to send the results to the faculty member. Once the students’ responses have been received the process of analysis continues in the same manner as that of an SGID (Ufland, 2020).

What It Does

The MSF has several clear benefits. For instructors, it provides nuanced, detailed feedback at a point in the semester when change is still possible. This feedback helps the instructor better understand the students’ experience and provides information about how to improve both the class environment and the learning process. Further, it helps build greater rapport between the instructor and students, as it shows students that the instructor values their input on the learning experience (Harris & Stevens, 2014; Holton, et al., 2016; McGrath, 2014). It also provides documentation both of teaching effectiveness and ongoing work to improve teaching effectiveness.

For students, it provides a forum to discuss their shared learning experience in the course and to identify shared challenges. Inasmuch as the instructor can make suggested changes, it can also enhance their learning experience. Perhaps most importantly, MSF provides a means of empowering students in the teaching and learning process. By providing students with an opportunity to collectively share their views and potentially effectuate change, students are given the chance to become active partners in the teaching and learning process and, in so doing, help improve their own learning outcomes (Berk, 2005; Harris & Stevens, 2013; Holton, et al., 2016; Kite, et al., 2015; Warner & Simmons, 2015).

Peter Ufland is Associate Professor of History at the University of the District of Columbia Community College. His latest research focuses on evaluation of history course syllabi in higher education and whether they conform to the best practices of teaching and learning.

Christian Aguiar is Assistant Professor of English at the University of the District of Columbia Community College. His research focuses on multimodal and collaborative assessments, approaches to teaching writing, and the experiences of first-generation and low-income college students in first-year composition. 

References

Berk, Ronald A. “Survey of 12 Strategies to Measure Teaching Effectiveness.” International Journal of Teaching and Learning 17 (2005), no. 1: 48-62.

Dangel, Harry and Lindsey, Peter. “What are Students (Really) Telling Us?” The Journal of Faculty Development 28 (2014), no. 2: 27-33.

George, Phillis L. “Reimagining the Student Evaluation: Using Democratic Frameworks in College Teaching and Learning.” In Willermet, Cathy, et al., Promoting Social Justice through the Scholarship of Teaching and Learning. Indiana University Press, 2017.

Gooblar, David. “The Midsemester Course Correction.” The Chronicle of Higher Education (October 31, 2017) https://www.chronicle.com/article/the-midsemester-course-correction/

Harris, G.L.A. and Stevens, Danielle D. “The Value of Midterm Student Feedback in Cross Disciplinary Graduate Programs.” Journal of Public Administration Education 19 (2013), no. 3: 537-558.

Holton, Doug, Hajara Mahmood, Kathyrn Cunningham, Miriam R. Diamond, Mary Wright, CRLT, University of Michigan, Maha Bali, Susan Brown, Eulises Dominguez. Midterm Student Feedback Guidebook. (2016) https://bit.ly/msfguidebook

Kahn, Susan. “Better Teaching Through Better Evaluation: A Guide for Faculty and Institutions.” To Improve the Academy 12 (1993), no. 1: 111-126.

Kite, Mary E., Prabin C. Subedi, Kinsey B. Bryant-Lees. “Student Perceptions of the Teaching Evaluation Process.” Teaching of Psychology 42 (2015), no. 4: 307-314.

McGrath, Laura B. “Mid-Semester Evaluations: How to Do Some Spring Cleaning in Your Classrooms.” Inside Higher Ed (March 11, 2014). https://www.insidehighered.com/blogs/gradhacker/mid-semester-evaluations

Medina, Brenda. “As Emphasis on Student Evaluations Grows, Professors Increasingly Seek Midcourse Feedback.” The Chronicle of Higher Education (October 30, 2011). https://www.chronicle.com/article/as-emphasis-on-student-evaluations-grows-professors-increasingly-seek-midcourse-feedback/

Nilson, Linda B. Teaching at its Best: A Research-Based Resource for College Instructors, 4^th ed. Jossey-Bass, 2016.

Ufland, Peter. “Empowering Students and Improving Learning Through Midterm Student Feedback.” The Cross Papers (2020), no. 23.

Veeck, Ann, Kelley O’Reilly, Amy MacMillan, Hongyan Yu. “The Use of Collaborative Student Evaluations to Provide Actionable Results.” Journal of Marketing Education 38 (2016), no. 3: 157-169.

Warner, Janis and Aneika Simmons. “Giving Voice to Students: A Preliminary Analysis of Midterm Evaluations and Procedural Justice.” Academy of Educational Leadership Journal 19 (2015), no. 1, 71-79.

Weimer, Maryellen. “Benefits of Talking with Students about Course Evaluations. Faculty Focus, June 15, 2016. https://www.facultyfocus.com/articles/faculty-development/benefits-talking-students-mid-course-evaluations/
Source link
April 7, 2025
Mid-Semester Course Corrections: Using the MSF Model to Engage Students and Improve Courses – Faculty Focus
In general, educators recognize the value of student feedback: it can help us better understand the classroom experience, modify learning activities, or adjust our policies and practices to improve student outcomes. Unfortunately, the most common form of feedback that instructors receive, the mandatory end-of-semester evaluation, is primarily intended to assess faculty rather than the teaching and learning experience. It rarely asks questions focused on student learning. Moreover, it takes place at a point in time that provides no opportunity to make the type of changes that might benefit the students who complete it (George, 2017; Holton, et al., 2016; Kahn, 1993; Medina, 2011; Nilson, 2016).

In contrast, the Midterm Student Feedback (MSF) mechanism, which is generally voluntary, is fundamentally focused on teaching and learning. The MSF asks students simple, direct questions focused on the teaching and learning process in their current course. Put more simply, it asks students to discuss what is and isn’t helping them learn (Dangel & Lindsey, 2014; Nelms, 2015; Payette & Brown, 2018; Ufland, 2020; Veeck, et al., 2016). The students’ feedback is then analyzed and used by the instructor to make any changes that he or she believes will help the students improve their learning outcomes over the course of the rest of the semester (Gooblar, 2017; Harris & Stevens, 2013; McGrath, 2014; Veeck, et al., 2016).

How It Works

The are several different forms of MSF that can be employed to attain feedback on the student learning process. For the purposes of clarity and simplicity, we feel that two are worth discussing here: the Small Group Instructional Diagnosis (SGID) and Bare Bones Questions (BBQ). While these methods vary in terms of the time and resources required to conduct them, all MSFs share certain key elements:
1. They must be conducted at a point in the semester when students have had enough time to become familiar with the course pedagogy, yet still early enough for meaningful changes to be made;
1. They must be a voluntary effort to improve learning, as opposed to an evaluation mandated by the institution;
1. They must be anonymous;
1. They must be conducted, analyzed, conveyed to the instructor, and discussed with the students in a timely fashion, usually within a week (Ufland, 2020).
Small Group Instructional Diagnosis (SGID)

The SGID is a collaborative form of MSF that requires the cooperation of a facilitator, typically a teaching and learning specialist or a trusted colleague. The process requires about thirty minutes of class time, so it is fairly efficient.

Before the evaluation, the facilitator and the instructor identify three or four open-ended questions to structure the MSF. The questions should encourage students to focus on the learning experience, not on the instructor. We’ve found the following questions effective: 
1. What in this course has helped to improve your learning? 
1. What in this course has hindered your learning? 
1. Is there anything you would like the instructor to change? 
On the day of the MSF, the instructor should introduce the facilitator and encourage students to be honest in their feedback, then leave the room. The facilitator then provides an overview of the process, divides students into groups of five to eight students, and asks them to discuss the three questions. 

Once students have worked through the questions together, the facilitator reconvenes the full group and elicits responses. The facilitator records all the responses for the class but also takes note of those points that seem to represent a consensus. The facilitator asks follow-up questions as needed to understand the context in which the responses are being offered. If the suggested questions above are used, the third can serve to synthesize responses. 

After the session, the facilitator writes a brief report synthesizing feedback from the class. The facilitator can use their judgment here: not all feedback needs to be shared, since the goal is to provide contextualized, actionable feedback. One way to achieve this more easily is to focus on those views that emerged as consensus points across the class. Indeed, one of the benefits of the MSF is that unhelpful feedback can be filtered out before the instructor sees it (Ufland, 2020). The facilitator may also provide a short, immediate “act now” list of items to the instructor and provide a more detailed report subsequently. This can enable the instructor to have an immediate discussion of the feedback with students, which is critical to the success of the model (Weimer, 2016).

Bare Bones Questions (BBQ)

The BBQ is a highly simplified form of midterm feedback that requires considerably less time than the more formal SGID. It also may be conducted without the assistance of a facilitator and can be effective in very small classes.

In this mode, the instructor provides written or digital copies of the questions – we suggest those listed above – to students near the end of a class session. The students then meet in small groups, compile their own responses, and elect one student to send the results to the faculty member. Once the students’ responses have been received the process of analysis continues in the same manner as that of an SGID (Ufland, 2020).

What It Does

The MSF has several clear benefits. For instructors, it provides nuanced, detailed feedback at a point in the semester when change is still possible. This feedback helps the instructor better understand the students’ experience and provides information about how to improve both the class environment and the learning process. Further, it helps build greater rapport between the instructor and students, as it shows students that the instructor values their input on the learning experience (Harris & Stevens, 2014; Holton, et al., 2016; McGrath, 2014). It also provides documentation both of teaching effectiveness and ongoing work to improve teaching effectiveness.

For students, it provides a forum to discuss their shared learning experience in the course and to identify shared challenges. Inasmuch as the instructor can make suggested changes, it can also enhance their learning experience. Perhaps most importantly, MSF provides a means of empowering students in the teaching and learning process. By providing students with an opportunity to collectively share their views and potentially effectuate change, students are given the chance to become active partners in the teaching and learning process and, in so doing, help improve their own learning outcomes (Berk, 2005; Harris & Stevens, 2013; Holton, et al., 2016; Kite, et al., 2015; Warner & Simmons, 2015).

Peter Ufland is Associate Professor of History at the University of the District of Columbia Community College. His latest research focuses on evaluation of history course syllabi in higher education and whether they conform to the best practices of teaching and learning.

Christian Aguiar is Assistant Professor of English at the University of the District of Columbia Community College. His research focuses on multimodal and collaborative assessments, approaches to teaching writing, and the experiences of first-generation and low-income college students in first-year composition. 

References

Berk, Ronald A. “Survey of 12 Strategies to Measure Teaching Effectiveness.” International Journal of Teaching and Learning 17 (2005), no. 1: 48-62.

Dangel, Harry and Lindsey, Peter. “What are Students (Really) Telling Us?” The Journal of Faculty Development 28 (2014), no. 2: 27-33.

George, Phillis L. “Reimagining the Student Evaluation: Using Democratic Frameworks in College Teaching and Learning.” In Willermet, Cathy, et al., Promoting Social Justice through the Scholarship of Teaching and Learning. Indiana University Press, 2017.

Gooblar, David. “The Midsemester Course Correction.” The Chronicle of Higher Education (October 31, 2017) https://www.chronicle.com/article/the-midsemester-course-correction/

Harris, G.L.A. and Stevens, Danielle D. “The Value of Midterm Student Feedback in Cross Disciplinary Graduate Programs.” Journal of Public Administration Education 19 (2013), no. 3: 537-558.

Holton, Doug, Hajara Mahmood, Kathyrn Cunningham, Miriam R. Diamond, Mary Wright, CRLT, University of Michigan, Maha Bali, Susan Brown, Eulises Dominguez. Midterm Student Feedback Guidebook. (2016) https://bit.ly/msfguidebook

Kahn, Susan. “Better Teaching Through Better Evaluation: A Guide for Faculty and Institutions.” To Improve the Academy 12 (1993), no. 1: 111-126.

Kite, Mary E., Prabin C. Subedi, Kinsey B. Bryant-Lees. “Student Perceptions of the Teaching Evaluation Process.” Teaching of Psychology 42 (2015), no. 4: 307-314.

McGrath, Laura B. “Mid-Semester Evaluations: How to Do Some Spring Cleaning in Your Classrooms.” Inside Higher Ed (March 11, 2014). https://www.insidehighered.com/blogs/gradhacker/mid-semester-evaluations

Medina, Brenda. “As Emphasis on Student Evaluations Grows, Professors Increasingly Seek Midcourse Feedback.” The Chronicle of Higher Education (October 30, 2011). https://www.chronicle.com/article/as-emphasis-on-student-evaluations-grows-professors-increasingly-seek-midcourse-feedback/

Nilson, Linda B. Teaching at its Best: A Research-Based Resource for College Instructors, 4^th ed. Jossey-Bass, 2016.

Payette, Patricia R., and Marie Kendall Brown. “Gathering Mid-Semester Feedback: Three Variations to Improve Instruction.” IDEA Paper No. 67, IDEA Center, https://eric.ed.gov/?id=ED588349

Ufland, Peter. “Empowering Students and Improving Learning Through Midterm Student Feedback.” The Cross Papers (2020), no. 23.

Veeck, Ann, Kelley O’Reilly, Amy MacMillan, Hongyan Yu. “The Use of Collaborative Student Evaluations to Provide Actionable Results.” Journal of Marketing Education 38 (2016), no. 3: 157-169.

Warner, Janis and Aneika Simmons. “Giving Voice to Students: A Preliminary Analysis of Midterm Evaluations and Procedural Justice.” Academy of Educational Leadership Journal 19 (2015), no. 1, 71-79.

Weimer, Maryellen. “Benefits of Talking with Students about Course Evaluations.” Faculty Focus, June 15, 2016. https://www.facultyfocus.com/articles/faculty-development/benefits-talking-students-mid-course-evaluations/
Source link
April 7, 2025
Can We Teach Talent? How Mindsets Can Foster Entrepreneurial Success – Faculty Focus
Can we teach entrepreneurship? This is a question that has vexed entrepreneurship scholars for generations, and it still is a question that is relevant today (Klein & Bullock, 2006). The Austrian economist Ludwig Von Mises (1949) who is one of the main precursorsof the field, argued that entrepreneurship is neither taught nor learned, for entrepreneurs see the world differently than others, perceiving what others don’t, breaking new ground where others are just “doing” what has been done before. As such, entrepreneurs come to be depicted as those who are dealing with novelty and complex scenarios, and they make decisions in ways that go beyond the “rational” models of normal social science. In many ways, entrepreneurship is treated as an inherent trait or skill that is not formalizable, and thus it is a behavioral trait, not an acquired skill.

This would fly in the face of the modern institutional environment of entrepreneurship research and education. For example, entrepreneurship is a field of study that is growing in business schools. Many currently offer minors in entrepreneurship or even majors, and some are offering PhD courses in Entrepreneurship to train the next generation of scholars and teachers in the field. Still, if Entrepreneurship is an inherent trait or talent, are we wasting resources and time, both of our students and ours, by attempting to teach what cannot be taught?

Such a question is not limited to the field of entrepreneurship itself. Entrepreneurship can be seen as a special case of the more general debate of “nurture” vs “nature” in teaching and learning. If intelligence and abilities are fixed from the outset, the teacher’s job becomes one of weeding out those who don’t have the innate potential and providing those with innate potential the information and tools to develop and refine what they already have. Your students will either be good Mathematicians, or they won’t. Actors will be good at playing a role, or they won’t. Singers will sing, or they will bother us making sounds we do not want to hear, etc, you can see the point. For both instructors and their students, that means adopting a mindset that avoids persistence in favor of pivoting and changing course. Early failures and failed first trials are not interpreted as a diagnosis of what could be done, and are instead seen as proof of what can’t (Yeager & Dweck, 2020).

This is where a growth mindset intervention has its place. Growth mindset theories question the claim that intelligence, talent, and skills are simply inherent traits of individuals, and see instead that education, training, and learning are not only tools to explore the potential that already exists, or even develop untapped potential, but they also may be the source of supposed “talent” (Yeager et al., 2020). With a growth mindset even talented individuals benefit and can advance their field, skills or capabilities. As Dweck (2018) showcased, even Olympic athletes benefit from a growth mindset. Athletes such as Michael Jordan and Tiger Woods pushed themselves, analyzed their performance, and addressed their weaknesses, achieving greater performance through persistent effort.

This example can be extended to different scenarios, such as music. A case of note is Avenged Sevenfold’s singer Matt Shadows. After a quick headstart into fame pioneering the Metalcore genre, Shadows quickly learned that technique and discipline are the crucial bedrock for his (in the author’s opinion) amazing singing abilities. In a recent interview for the Finnish magazine Chaoszine (2024), he mentioned how he developed a set of routines to better his singing that range from a well-planned diet to the way he sleeps. If talent was all he had, he would simply get on stage and sing. Instead, he developed what can be seen as a growth mindset where consistent training, practice, and care were the bedrock for what he did.

Where does entrepreneurship come into play in all of this? As my argument goes, for starters even if talent is somewhat inherent, it will not turn into successful outcomes if not for the possession of a growth mindset. Beyond that, even if not first showcased as talent, skills do emerge if effort, motivated by a growth mindset, is present. Research has showcased how a growth mindset improves entrepreneurial self-efficacy (defined as the beliefs one has about his ability to bring about a desired outcome) in higher education students, increasing their task persistence and career interest (Burnette et al, 2020). In other settings similar results were reported, where growth mindset interventions created positive feedback loops in academic performance (Limeri et al., 2020). Both cases reinforce the argument that for entrepreneurs or students reducing how much failure impacts decisions to persist is crucial if capabilities to succeed are to be achieved, and entrepreneurship education would benefit from taking a more experimental, trial-based approach strengthened by a growth mindset.

For us teachers, that implies that our role is greater than what a “nature” based perspective on education would allow. Teaching and learning now have more than a dimension of giving students knowledge that they’ll turn into what they are already capable of turning it into (basically showing a student who already has a propensity to be good in Mathematics what calculus is). Education now becomes a quest for developing in students the skills, discipline and mindset needed for their successful learning paths. In less abstract terms, teaching is not reproducing knowledge, but also teaching how to acquire knowledge or how to start a practice (Seaton, 2017).

For entrepreneurship educators, that is translated into a competence development approach for how they educate their students. The intended outcome (entrepreneurial value-creating activity) has as its input the myriad of competencies developed in training for taking up the tasks and challenges associated with the entrepreneurial journey (Mawson & Simmons, 2023). As previous research showcases (Camuffo et al., 2020), entrepreneurs become comparatively more successful when failures are absorbed and used as a means for diagnosing what needs to be changed. Entrepreneurs, much like academics and students, are working in a trial-and-error environment where what is important is not to immediately succeed. Immediate success, although desirable, does not let the entrepreneur (or student) learn how to adapt to unforeseen changes and failures.

For educators in general, the situation is not that much different. We need to show our students that they can be more than what their current limits impose on them, and that is where a growth mindset intervention comes into play. Mathematics, entrepreneurship, and acting, should not be inherently reserved for those who showcased these skills from the outset. Students can grow into those roles by using failure as a form of diagnosis for future learning. One of the ways to do such is to realize such interventions in the classroom entails using coursework as a way of showcasing how students may change their own self-efficacy (as their self-perceived ability to achieve a goal or complete a task). Here are general principles for growth-mindset interventions:
1. Use assignments at a greater frequency to assess progress and give early feedback (Ex: Weekly mini-tests come into play here).
1. When providing feedback, not only say what was wrong but provide a clear blueprint of what the student can do to correct previous mistakes (Ex: When delivering a corrected test to the student, consider handling it with a note on his strengths and weaknesses, and what you would like him to study to avoid the same mistakes on the next test).
1. Emphasize how past failure can be traded for future success through persistent trials. (Ex: Cumulative tests can be useful here, where knowledge from one assignment builds into the next one).
They can be seen as practical cues for teaching students, likely entrepreneurs or not. In education, science or business, failures may be seen as negative feedback in the short-run, but can be used as a tool for positive improvement in the long-run. Students and entrepreneurs with a growth mindset do not see failure as a simple one-time event that signals the need to pivot away from the subject, topic or field. Instead, a growth mindset enables them to interpret failure as a learning cue that showcases the need to change their strategies, reassess their assumptions and reallocate their efforts to try again. Mathematics, entrepreneurship, and acting, should not be inherently reserved for those who showcased these skills from the outset, and fear from failure should not stop someone from trying. Once again, it is to be emphasized that students can grow into those roles through persistent (but directed) effort, and we better help them try, for how many might-have-beens could have been realized, if not for a lack of a growth mindset?

Luan Valério is a PhD Student in Entrepreneurship at Baylor University’s Hankamer School of Business in Texas, having previously obtained a BA in Economics back in his home country, Brazil. His research interests encompass strategic entrepreneurship, decision-making under uncertainty and entrepreneurial education.

References

Camuffo, A., Cordova, A., Gambardella, A., Spina, C. (2020). A Scientific Approach to Entrepreneurial Decision Making: Evidence from a Randomized Control Trial. Management Science, 66(2), 564-586.

Chaoszine Magazine. (2024). My Story As Metal Frontman #87: M.Shadows (Avenged Sevenfold). In: https://www.youtube.com/watch?v=tpYgwdE82jo

Dweck, Carol. (2018) Mindsets: Developing Talent Through a Growth Mindset. Olympic Coach, 7, n.21.

Limeri, L.B., Carter, N.T., Choe, J. et al. (2020). Growing a growth mindset: characterizing how and why undergraduate students’ mindsets change. IJ STEM Ed 7, 35.

Yeager, D. S., & Dweck, C. S. (2020). What can be learned from growth mindset controversies? American Psychologist, 75(9), 1269–1284.

Burnette, J. L., Pollack, J. M., Forsyth, R. B., Hoyt, C. L., Babij, A. D., Thomas, F. N., & Coy, A. E. (2020). A Growth Mindset Intervention: Enhancing Students’ Entrepreneurial Self-Efficacy and Career Development. Entrepreneurship Theory and Practice, 44(5), 878- 908.

Klein, Peter G. & Bullock, J. Bruce, (2006). “Can Entrepreneurship Be Taught?,” Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 38(2), pages 1-11.

Klein, Peter G. & Bylund, Per. (2014). The Place of Austrian Economics in Contemporary Entrepreneurship Research. Review of Austrian Economics, 27, 259-279.

Mises, Ludwig Von. (1949). Human Action: A Treatise on Economics. The Ludwig Von Mises Institute.

Mawson, S., Casulli, L., & Simmons, E. L. (2023). A Competence Development Approach for Entrepreneurial Mindset in Entrepreneurship Education. Entrepreneurship Education and Pedagogy, 6(3), 481-501.

Seaton, F. S. (2017). Empowering teachers to implement a growth mindset. Educational Psychology in Practice, 34(1), 41–57.
Source link
April 4, 2025
Can We Teach Talent? How Mindsets Can Foster Entrepreneurial Success – Faculty Focus
Can we teach entrepreneurship? This is a question that has vexed entrepreneurship scholars for generations, and it still is a question that is relevant today (Klein & Bullock, 2006). The Austrian economist Ludwig Von Mises (1949) who is one of the main precursorsof the field, argued that entrepreneurship is neither taught nor learned, for entrepreneurs see the world differently than others, perceiving what others don’t, breaking new ground where others are just “doing” what has been done before. As such, entrepreneurs come to be depicted as those who are dealing with novelty and complex scenarios, and they make decisions in ways that go beyond the “rational” models of normal social science. In many ways, entrepreneurship is treated as an inherent trait or skill that is not formalizable, and thus it is a behavioral trait, not an acquired skill.

This would fly in the face of the modern institutional environment of entrepreneurship research and education. For example, entrepreneurship is a field of study that is growing in business schools. Many currently offer minors in entrepreneurship or even majors, and some are offering PhD courses in Entrepreneurship to train the next generation of scholars and teachers in the field. Still, if Entrepreneurship is an inherent trait or talent, are we wasting resources and time, both of our students and ours, by attempting to teach what cannot be taught?

Such a question is not limited to the field of entrepreneurship itself. Entrepreneurship can be seen as a special case of the more general debate of “nurture” vs “nature” in teaching and learning. If intelligence and abilities are fixed from the outset, the teacher’s job becomes one of weeding out those who don’t have the innate potential and providing those with innate potential the information and tools to develop and refine what they already have. Your students will either be good Mathematicians, or they won’t. Actors will be good at playing a role, or they won’t. Singers will sing, or they will bother us making sounds we do not want to hear, etc, you can see the point. For both instructors and their students, that means adopting a mindset that avoids persistence in favor of pivoting and changing course. Early failures and failed first trials are not interpreted as a diagnosis of what could be done, and are instead seen as proof of what can’t (Yeager & Dweck, 2020).

This is where a growth mindset intervention has its place. Growth mindset theories question the claim that intelligence, talent, and skills are simply inherent traits of individuals, and see instead that education, training, and learning are not only tools to explore the potential that already exists, or even develop untapped potential, but they also may be the source of supposed “talent” (Yeager et al., 2020). With a growth mindset even talented individuals benefit and can advance their field, skills or capabilities. As Dweck (2018) showcased, even Olympic athletes benefit from a growth mindset. Athletes such as Michael Jordan and Tiger Woods pushed themselves, analyzed their performance, and addressed their weaknesses, achieving greater performance through persistent effort.

This example can be extended to different scenarios, such as music. A case of note is Avenged Sevenfold’s singer Matt Shadows. After a quick headstart into fame pioneering the Metalcore genre, Shadows quickly learned that technique and discipline are the crucial bedrock for his (in the author’s opinion) amazing singing abilities. In a recent interview for the Finnish magazine Chaoszine (2024), he mentioned how he developed a set of routines to better his singing that range from a well-planned diet to the way he sleeps. If talent was all he had, he would simply get on stage and sing. Instead, he developed what can be seen as a growth mindset where consistent training, practice, and care were the bedrock for what he did.

Where does entrepreneurship come into play in all of this? As my argument goes, for starters even if talent is somewhat inherent, it will not turn into successful outcomes if not for the possession of a growth mindset. Beyond that, even if not first showcased as talent, skills do emerge if effort, motivated by a growth mindset, is present. Research has showcased how a growth mindset improves entrepreneurial self-efficacy (defined as the beliefs one has about his ability to bring about a desired outcome) in higher education students, increasing their task persistence and career interest (Burnette et al, 2020). In other settings similar results were reported, where growth mindset interventions created positive feedback loops in academic performance (Limeri et al., 2020). Both cases reinforce the argument that for entrepreneurs or students reducing how much failure impacts decisions to persist is crucial if capabilities to succeed are to be achieved, and entrepreneurship education would benefit from taking a more experimental, trial-based approach strengthened by a growth mindset.

For us teachers, that implies that our role is greater than what a “nature” based perspective on education would allow. Teaching and learning now have more than a dimension of giving students knowledge that they’ll turn into what they are already capable of turning it into (basically showing a student who already has a propensity to be good in Mathematics what calculus is). Education now becomes a quest for developing in students the skills, discipline and mindset needed for their successful learning paths. In less abstract terms, teaching is not reproducing knowledge, but also teaching how to acquire knowledge or how to start a practice (Seaton, 2017).

For entrepreneurship educators, that is translated into a competence development approach for how they educate their students. The intended outcome (entrepreneurial value-creating activity) has as its input the myriad of competencies developed in training for taking up the tasks and challenges associated with the entrepreneurial journey (Mawson & Simmons, 2023). As previous research showcases (Camuffo et al., 2020), entrepreneurs become comparatively more successful when failures are absorbed and used as a means for diagnosing what needs to be changed. Entrepreneurs, much like academics and students, are working in a trial-and-error environment where what is important is not to immediately succeed. Immediate success, although desirable, does not let the entrepreneur (or student) learn how to adapt to unforeseen changes and failures.

For educators in general, the situation is not that much different. We need to show our students that they can be more than what their current limits impose on them, and that is where a growth mindset intervention comes into play. Mathematics, entrepreneurship, and acting, should not be inherently reserved for those who showcased these skills from the outset. Students can grow into those roles by using failure as a form of diagnosis for future learning. One of the ways to do such is to realize such interventions in the classroom entails using coursework as a way of showcasing how students may change their own self-efficacy (as their self-perceived ability to achieve a goal or complete a task). Here are general principles for growth-mindset interventions:
1. Use assignments at a greater frequency to assess progress and give early feedback (Ex: Weekly mini-tests come into play here).
1. When providing feedback, not only say what was wrong but provide a clear blueprint of what the student can do to correct previous mistakes (Ex: When delivering a corrected test to the student, consider handling it with a note on his strengths and weaknesses, and what you would like him to study to avoid the same mistakes on the next test).
1. Emphasize how past failure can be traded for future success through persistent trials. (Ex: Cumulative tests can be useful here, where knowledge from one assignment builds into the next one).
They can be seen as practical cues for teaching students, likely entrepreneurs or not. In education, science or business, failures may be seen as negative feedback in the short-run, but can be used as a tool for positive improvement in the long-run. Students and entrepreneurs with a growth mindset do not see failure as a simple one-time event that signals the need to pivot away from the subject, topic or field. Instead, a growth mindset enables them to interpret failure as a learning cue that showcases the need to change their strategies, reassess their assumptions and reallocate their efforts to try again. Mathematics, entrepreneurship, and acting, should not be inherently reserved for those who showcased these skills from the outset, and fear from failure should not stop someone from trying. Once again, it is to be emphasized that students can grow into those roles through persistent (but directed) effort, and we better help them try, for how many might-have-beens could have been realized, if not for a lack of a growth mindset?

Luan Valério is a PhD Student in Entrepreneurship at Baylor University’s Hankamer School of Business in Texas, having previously obtained a BA in Economics back in his home country, Brazil. His research interests encompass strategic entrepreneurship, decision-making under uncertainty and entrepreneurial education.

References

Camuffo, A., Cordova, A., Gambardella, A., Spina, C. (2020). A Scientific Approach to Entrepreneurial Decision Making: Evidence from a Randomized Control Trial. Management Science, 66(2), 564-586.

Chaoszine Magazine. (2024). My Story As Metal Frontman #87: M.Shadows (Avenged Sevenfold). In: https://www.youtube.com/watch?v=tpYgwdE82jo

Dweck, Carol. (2018) Mindsets: Developing Talent Through a Growth Mindset. Olympic Coach, 7, n.21.

Limeri, L.B., Carter, N.T., Choe, J. et al. (2020). Growing a growth mindset: characterizing how and why undergraduate students’ mindsets change. IJ STEM Ed 7, 35.

Yeager, D. S., & Dweck, C. S. (2020). What can be learned from growth mindset controversies? American Psychologist, 75(9), 1269–1284.

Burnette, J. L., Pollack, J. M., Forsyth, R. B., Hoyt, C. L., Babij, A. D., Thomas, F. N., & Coy, A. E. (2020). A Growth Mindset Intervention: Enhancing Students’ Entrepreneurial Self-Efficacy and Career Development. Entrepreneurship Theory and Practice, 44(5), 878- 908.

Klein, Peter G. & Bullock, J. Bruce, (2006). “Can Entrepreneurship Be Taught?,” Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 38(2), pages 1-11.

Klein, Peter G. & Bylund, Per. (2014). The Place of Austrian Economics in Contemporary Entrepreneurship Research. Review of Austrian Economics, 27, 259-279.

Mises, Ludwig Von. (1949). Human Action: A Treatise on Economics. The Ludwig Von Mises Institute.

Mawson, S., Casulli, L., & Simmons, E. L. (2023). A Competence Development Approach for Entrepreneurial Mindset in Entrepreneurship Education. Entrepreneurship Education and Pedagogy, 6(3), 481-501.

Seaton, F. S. (2017). Empowering teachers to implement a growth mindset. Educational Psychology in Practice, 34(1), 41–57.
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April 4, 2025
Beyond Evaluation: Using Peer Observation to Strengthen Teaching Practices – Faculty Focus
University faculty members are usually immersed in various tasks related to teaching, research, committees, and office hours. As a result, they rarely have time to explore the practices of their colleagues or seek advice on their own, which leads them to work in isolation. This isolation can hinder their professional development and growth, especially if they do not take the time to reflect on their own teaching methods and practices. Ultimately, student learning is impacted. One effective practice that can break this cycle is peer observation.

Peer observation is a valuable university practice with mutual benefits for the observer and observee, with an emphasis on learning, reflection, and improvement to enhance teaching methods and overall practices. Exploring peer observation models, their stages, and practical methods and resources for use in higher education are all part of this process. By fostering a culture of continuous improvement through pedagogical observation, educators engage in dialogue to share insights, plan for instructional improvement, self-reflect, adopt effective strategies, and refine teaching practices.

Models of Peer Observation

The focus of the peer observation defines its goal, context, and the way the participants act and feel toward the process. Based on the focus, peer observations can be classified into two models: formative-developmental and summative-evaluative.

While the formative-developmental observation aims to enhance and promote the improvement of teaching skills within a supportive and collegial context, the summative-evaluative observation aims to evaluate and judge the faculty based on the observation. Therefore, if an institution aims at improving the teaching practices of its faculty members and wants them to engage in professional development activities that they welcome and accept, it should incorporate a formative-developmental type of observation.

Although summative-evaluative peer observations are needed to evaluate the teaching skills of faculty members, we believe that it is a fair practice for the faculty to experience formative-developmental observations several times and to receive the necessary support before they undergo an evaluative observation especially if it is high-stakes in that it impacts their job retention or promotion. In our discussion, we will focus on formative-developmental observations because we are interested in practices that help in the improvement of teaching practices in educational institutions.

Benefits of Formative-developmental Observations

When approached as a developmental or formative practice, peer observations afford faculty valuable opportunities to improve their practices and grow professionally. This is mainly true if observations are done periodically, rather than as one-shot classroom visits, when the outcomes of one observation feed into another, and when on-going dialogue between the observer and the observee takes place. The practices of the process of classroom observations are critical as they:
- Encourage professional growth: Formative observations provide faculty with structured opportunities to enhance their teaching skills and improve student learning outcomes.
- Facilitate reflective practice: Formative observations enable faculty to critically assess their teaching methods and refine their instructional approaches.
- Support learning in and from practice: As highlighted by Ball and Cohen (1999), learning is most effective when embedded in practice, making peer observations a powerful tool for professional development.
- Promote the haring of best practices: Faculty can observe, discuss, and integrate effective teaching strategies used by their peers.
- Challenge teaching assumptions: Through peer feedback and reflection, faculty can identify and modify ingrained teaching habits to enhance student engagement.
- Build confidence in implementing new strategies: Observing peers in action helps faculty see new techniques in practice, making them more confident in adopting these approaches.
- Enhance course content and delivery: observations allow for a structured review of both the material taught and the teaching methods used, leading to refinements in instruction.
- Strengthen collegial support and collaboration: Collegial observations enhance a culture of open dialogue among faculty, fostering a supportive and collaborative academic environment.
- Improve teaching and learning quality: Observations encourage continuous improvement in instructional effectiveness, ultimately benefiting student learning experiences.
Stages of Formative-developmental Observations

Peer observations have numerous merits. They can be perceived as a cyclic process, each cycle including a peer observation session that is preceded and followed by meetings between the observer and the faculty whose class is to be observed, thus making it a three-stage process. Each of the stages has its benefits and contributes to the overall outcome of the process if done with a clear purpose in a safe, collegial, and supportive context. The three stages are:
- Pre-observation: To introduce the process and set the stage for effective peer observation, it is necessary to discuss the observation goals and establish mutual expectations for the process, in addition to discussing the session learning outcomes, learning activities and any content or documents that will be used during the observed session. This stage helps the observer clarify the goal of the observation and establishes an atmosphere of collaboration between the observer and the observee which reduces anxiety on the part of the observee. Moreover, it allows the observee to clarify the lesson context and allows for customization of the lesson before the observation takes place.
- Lesson Observation: This includes observing a teaching session with all its happenings such as teaching strategies, students’ engagement, modes of interaction, indicators of learning, assessment tasks, questioning techniques, and learning activities. At this stage, the observer collects notes describing what is observed without making any judgment about the performance of the teacher. To this end, we prefer using the narrative log as it enables the observer to document the timing of behaviors, techniques, and actions of both the observed instructor and the students. These are recorded objectively; i.e., recording solely what is observed without including any of the observer’s judgments. However, the observer can add comments or questions about the classroom events to be discussed later in the post-observation meeting. Narrative logs are considered an effective tool to describe verbal and non-verbal behavior, highlighting what the observer sees rather than making judgements (Chism, 2007). To this end, the observer must keep in mind the golden rule: Learning to see, unlearning to judge.
- Post- observation: At this stage, discussion of the class activities and reflection occurs. The observer uses the observation notes collected during the lesson observation to lead the discussion and to refer to for evidence for the different points being discussed. The observee reflects on the class; and the observer, to provide helpful feedback, seeks to understand more about the classroom by asking questions for clarification or to gain a clearer understanding of the lesson, students, and content. An excellent tool designed by Project-Zero at Harvard University is the Ladder of Feedback, which can help structure the discussion to be constructive, supportive and growth-promoting in a non-threatening manner.
The post-observation meeting should occur as soon as possible after the observation. The discussion in this stage should lead to an action plan. Therefore, as a good practice, the observer can summarize the points discussed in this meeting, emphasize points of strength, provide suggestions toward improving teaching practices, and agree with the observee on an action plan for improvement. The date for the following observation or meeting can be scheduled at the end of this stage.

How to Encourage Peer Observation in Higher Education

Although peer observation is a vital practice for professional development, it is not well-perceived by faculty members, and it is not a common practice in universities. Faculty members are mostly reluctant to voluntarily participate in peer observation. Thus, we recommend that universities start developing a culture of trust and collegiality to support this practice. At the personal level of faculty members, they can be encouraged to ask their colleagues to observe their classrooms and collect notes during the observations. To sharpen this practice and make it more relevant to one’s instructional needs, the observee can set specific goals for the observation. These suggestions can help promote the culture where peer observation becomes a practice for continuous, accessible professional development that can transform universities into learning communities and make them better learning spaces for their faculty members.

Dr. Siham Al Amoush is an experienced educator specializing in teacher education and higher education professional development, she obtained her PhD in Chemistry Education from Bremen University, where she researched and compared teacher beliefs in Jordan, Turkey, and Germany. She has published several papers in international journals on topics such as teacher beliefs, curriculum design, and technology integration in the classroom. Passionate about improving teacher education and supporting educators to reach their full potential, she aims to make a difference in the education system through collaboration between different sectors. Currently, she works in faculty development at the Excellence in Teaching and Learning Center at Qatar University.

Dr. Amal Farhat is an experienced educator and instructional coach with a background in both K-12 and higher education across the private and public sectors. She is interested in fostering professional learning communities through positive, constructive collaboration among all institutional stakeholders. In higher education, she has taught future educators and supported faculty in enhancing their teaching practices. Dr. Farhat holds a PhD in Education from the Lebanese University, a Counseling in Pedagogy diploma from St. Joseph University, and a CAEL certification from Harvard Graduate School of Education.
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April 2, 2025
Beyond Evaluation: Using Peer Observation to Strengthen Teaching Practices – Faculty Focus
University faculty members are usually immersed in various tasks related to teaching, research, committees, and office hours. As a result, they rarely have time to explore the practices of their colleagues or seek advice on their own, which leads them to work in isolation. This isolation can hinder their professional development and growth, especially if they do not take the time to reflect on their own teaching methods and practices. Ultimately, student learning is impacted. One effective practice that can break this cycle is peer observation.

Peer observation is a valuable university practice with mutual benefits for the observer and observee, with an emphasis on learning, reflection, and improvement to enhance teaching methods and overall practices. Exploring peer observation models, their stages, and practical methods and resources for use in higher education are all part of this process. By fostering a culture of continuous improvement through pedagogical observation, educators engage in dialogue to share insights, plan for instructional improvement, self-reflect, adopt effective strategies, and refine teaching practices.

Models of Peer Observation

The focus of the peer observation defines its goal, context, and the way the participants act and feel toward the process. Based on the focus, peer observations can be classified into two models: formative-developmental and summative-evaluative.

While the formative-developmental observation aims to enhance and promote the improvement of teaching skills within a supportive and collegial context, the summative-evaluative observation aims to evaluate and judge the faculty based on the observation. Therefore, if an institution aims at improving the teaching practices of its faculty members and wants them to engage in professional development activities that they welcome and accept, it should incorporate a formative-developmental type of observation.

Although summative-evaluative peer observations are needed to evaluate the teaching skills of faculty members, we believe that it is a fair practice for the faculty to experience formative-developmental observations several times and to receive the necessary support before they undergo an evaluative observation especially if it is high-stakes in that it impacts their job retention or promotion. In our discussion, we will focus on formative-developmental observations because we are interested in practices that help in the improvement of teaching practices in educational institutions.

Benefits of Formative-developmental Observations

When approached as a developmental or formative practice, peer observations afford faculty valuable opportunities to improve their practices and grow professionally. This is mainly true if observations are done periodically, rather than as one-shot classroom visits, when the outcomes of one observation feed into another, and when on-going dialogue between the observer and the observee takes place. The practices of the process of classroom observations are critical as they:
- Encourage professional growth: Formative observations provide faculty with structured opportunities to enhance their teaching skills and improve student learning outcomes.
- Facilitate reflective practice: Formative observations enable faculty to critically assess their teaching methods and refine their instructional approaches.
- Support learning in and from practice: As highlighted by Ball and Cohen (1999), learning is most effective when embedded in practice, making peer observations a powerful tool for professional development.
- Promote the haring of best practices: Faculty can observe, discuss, and integrate effective teaching strategies used by their peers.
- Challenge teaching assumptions: Through peer feedback and reflection, faculty can identify and modify ingrained teaching habits to enhance student engagement.
- Build confidence in implementing new strategies: Observing peers in action helps faculty see new techniques in practice, making them more confident in adopting these approaches.
- Enhance course content and delivery: observations allow for a structured review of both the material taught and the teaching methods used, leading to refinements in instruction.
- Strengthen collegial support and collaboration: Collegial observations enhance a culture of open dialogue among faculty, fostering a supportive and collaborative academic environment.
- Improve teaching and learning quality: Observations encourage continuous improvement in instructional effectiveness, ultimately benefiting student learning experiences.
Stages of Formative-developmental Observations

Peer observations have numerous merits. They can be perceived as a cyclic process, each cycle including a peer observation session that is preceded and followed by meetings between the observer and the faculty whose class is to be observed, thus making it a three-stage process. Each of the stages has its benefits and contributes to the overall outcome of the process if done with a clear purpose in a safe, collegial, and supportive context. The three stages are:
- Pre-observation: To introduce the process and set the stage for effective peer observation, it is necessary to discuss the observation goals and establish mutual expectations for the process, in addition to discussing the session learning outcomes, learning activities and any content or documents that will be used during the observed session. This stage helps the observer clarify the goal of the observation and establishes an atmosphere of collaboration between the observer and the observee which reduces anxiety on the part of the observee. Moreover, it allows the observee to clarify the lesson context and allows for customization of the lesson before the observation takes place.
- Lesson Observation: This includes observing a teaching session with all its happenings such as teaching strategies, students’ engagement, modes of interaction, indicators of learning, assessment tasks, questioning techniques, and learning activities. At this stage, the observer collects notes describing what is observed without making any judgment about the performance of the teacher. To this end, we prefer using the narrative log as it enables the observer to document the timing of behaviors, techniques, and actions of both the observed instructor and the students. These are recorded objectively; i.e., recording solely what is observed without including any of the observer’s judgments. However, the observer can add comments or questions about the classroom events to be discussed later in the post-observation meeting. Narrative logs are considered an effective tool to describe verbal and non-verbal behavior, highlighting what the observer sees rather than making judgements (Chism, 2007). To this end, the observer must keep in mind the golden rule: Learning to see, unlearning to judge.
- Post- observation: At this stage, discussion of the class activities and reflection occurs. The observer uses the observation notes collected during the lesson observation to lead the discussion and to refer to for evidence for the different points being discussed. The observee reflects on the class; and the observer, to provide helpful feedback, seeks to understand more about the classroom by asking questions for clarification or to gain a clearer understanding of the lesson, students, and content. An excellent tool designed by Project-Zero at Harvard University is the Ladder of Feedback, which can help structure the discussion to be constructive, supportive and growth-promoting in a non-threatening manner.
The post-observation meeting should occur as soon as possible after the observation. The discussion in this stage should lead to an action plan. Therefore, as a good practice, the observer can summarize the points discussed in this meeting, emphasize points of strength, provide suggestions toward improving teaching practices, and agree with the observee on an action plan for improvement. The date for the following observation or meeting can be scheduled at the end of this stage.

How to Encourage Peer Observation in Higher Education

Although peer observation is a vital practice for professional development, it is not well-perceived by faculty members, and it is not a common practice in universities. Faculty members are mostly reluctant to voluntarily participate in peer observation. Thus, we recommend that universities start developing a culture of trust and collegiality to support this practice. At the personal level of faculty members, they can be encouraged to ask their colleagues to observe their classrooms and collect notes during the observations. To sharpen this practice and make it more relevant to one’s instructional needs, the observee can set specific goals for the observation. These suggestions can help promote the culture where peer observation becomes a practice for continuous, accessible professional development that can transform universities into learning communities and make them better learning spaces for their faculty members.

Dr. Siham Al Amoush is an experienced educator specializing in teacher education and higher education professional development, she obtained her PhD in Chemistry Education from Bremen University, where she researched and compared teacher beliefs in Jordan, Turkey, and Germany. She has published several papers in international journals on topics such as teacher beliefs, curriculum design, and technology integration in the classroom. Passionate about improving teacher education and supporting educators to reach their full potential, she aims to make a difference in the education system through collaboration between different sectors. Currently, she works in faculty development at the Excellence in Teaching and Learning Center at Qatar University.

Dr. Amal Farhat is an experienced educator and instructional coach with a background in both K-12 and higher education across the private and public sectors. She is interested in fostering professional learning communities through positive, constructive collaboration among all institutional stakeholders. In higher education, she has taught future educators and supported faculty in enhancing their teaching practices. Dr. Farhat holds a PhD in Education from the Lebanese University, a Counseling in Pedagogy diploma from St. Joseph University, and a CAEL certification from Harvard Graduate School of Education.
Source link
April 2, 2025
AI-Powered Teaching: Practical Tools for Community College Faculty – Faculty Focus
Artificial intelligence (AI) has transitioned from a speculative concept to a transformative tool in higher education, particularly within community colleges. This article explores AI’s historical evolution, critically assesses its benefits and challenges for teaching, and provides evidence-based strategies for faculty to integrate AI effectively. Drawing on the scholarship of teaching and learning (SoTL), it argues that AI can enhance accessibility and efficiency while preserving the human essence of education. A case study from a California community college illustrates these principles in action. Faculty developers and instructors can use this framework to harness AI’s potential, ensuring it supports rather than supplants their pedagogical roles.

Introduction

In Ayad Akhtar’s 2023 play McNeal, ChatGPT performs alongside Robert Downey Jr., a theatrical milestone reflecting artificial intelligence’s (AI) encroachment into domains once deemed uniquely human (Akhtar, 2023, as cited in “From Čapek to ChatGPT”). This echoes Karel Čapek’s R.U.R. (Rossum’s Universal Robots), where artificial beings, created to serve, ultimately undo their creators (Čapek, 1920/2004). A century later, AI is no longer fiction but a dynamic force in community college education, reshaping how faculty teach, and students learn. For instructors serving diverse, often underserved populations, AI raises critical questions: How has it evolved into a pedagogical tool? What are its merits and limitations? How can it enhance teaching without diminishing faculty agency?

This article addresses these questions through a three-part exploration: tracing AI’s historical development, critically evaluating its role in community college classrooms, and offering practical, evidence-based strategies for faculty. Grounded in the scholarship of teaching and learning (SoTL), it expands on prior work (e.g., Davidson, 2021; Pritts, 2023) to position AI as a partner in faculty development. A case study illustrates these principles in practice, aligning with Faculty Focus’s mission to improve teaching and student success in higher education.

Historical Context: AI’s Evolution and Educational Implications

AI’s journey from myth to modernity informs its current educational applications. Ancient tales of Talos, a bronze automaton guarding Europa, and Pygmalion’s animated sculpture reveal humanity’s early fascination with artificial life (Hamilton, 1942). By the 3rd century BC, Greek inventors like Archytas built the steam-powered Flying Pigeon, an early self-operating device (Berryman, 2003). The 18th-century Jaquet-Droz automata—mechanical dolls mimicking human actions—furthered this vision (Riskin, 2016). Philosophers like Descartes and Leibniz speculated on machine cognition, while 19th-century pioneers Babbage and Lovelace laid computing’s foundations (Copeland, 2012).

The modern AI field emerged in 1950 when Alan Turing posed, “Can machines think?” (Turing, 1950), followed by John McCarthy’s naming of “artificial intelligence” (McCarthy, 2007). Early systems like MYCIN, which diagnosed infections using rule-based logic, showcased potential but lacked adaptability (Buchanan & Shortliffe, 1984). The 1990s introduced machine learning, enabling AI to learn from data—email spam filters improved iteratively, marking a shift from static programming (Mitchell, 1997). The 2010s brought deep learning, with neural networks emulating human cognition; Google’s AlphaGo defeated Go champion Lee Sedol in 2016, devising strategies its creators couldn’t predict (Silver et al., 2016).

Since 2020, generative AI—exemplified by OpenAI’s GPT-3 and ChatGPT—has produced human-like text, transforming fields from healthcare to education (Spitale et al., 2023; Topol, 2019). In classrooms, AI’s trajectory is evident: 1980s grading software evolved into 2020s virtual tutors, shifting from peripheral aids to central tools (Baylor & Ryu, 2003). For community college faculty, this history contextualizes AI’s rapid ascent, highlighting its capacity to augment teaching—yet it also underscores the need for critical oversight, a theme explored next.

AI’s integration into community colleges offers transformative potential but demands scrutiny, particularly given the sector’s diverse student body—often first-generation, working adults, or developmental learners (Cohen et al., 2014).

Benefits: Accessibility and Faculty Efficiency
AI enhances accessibility, a cornerstone of community college missions. Hybrid and HyFlex models, supported by AI-driven platforms like Zoom, allow asynchronous learning, vital for students balancing work and family (Quality Matters, 2022). A 2022 report documented a 220% surge in preference for online/hybrid courses (from 9% in 2020 to 29% in 2022), reflecting AI’s role in broadening access (Kelly, 2022, as cited in Quality Matters, 2022). Faculty benefit too: automated grading reduces routine burdens, while NLP tools like ChatGPT provide instant student support, amplifying instructors’ reach (Barnes & Noble Education, 2022; Topol, 2019).

Challenges: Engagement and Skill Development
However, AI’s efficacy hinges on student self-regulation, a persistent challenge where developmental courses predominate (Zimmerman, 2002). Virtual settings often lack in-person dynamics—non-verbal cues, spontaneous discussions—potentially weakening engagement (Quality Matters, 2022). Faculty unease is notable: 54% prefer fully in-person instruction, citing discomfort with digital mediation (Barnes & Noble Education, 2022). Overreliance on AI risks undermining critical thinking; Pritts (2023) warns that tools generating papers may erode analytical skills, echoing SoTL calls for active learning (Davidson, 2021).

Ethical Considerations: Equity and Agency
Ethical dilemmas loom large. Algorithmic bias in grading systems can exacerbate inequities, disproportionately affecting marginalized learners (O’Neil, 2016). Access disparities—unreliable internet or devices—threaten equitable implementation (Means et al., 2020). Spitale et al. (2023) highlight AI’s dual edge: it clarifies concepts but also crafts compelling disinformation, posing risks to academic integrity. Without oversight, AI could shift from facilitator to arbiter, diminishing faculty agency—a concern rooted in Čapek’s cautionary tale (1920/2004). Faculty development must address these tensions to ensure AI serves educational goals.

AI and Academic Integrity: Redrawing Assessment
The rise of AI tools like ChatGPT has forced educators to rethink traditional assessment methods. As discussed in Inside Higher Ed (2025), the prevalence of AI-generated content has made it increasingly difficult to distinguish between student work and machine-generated output. This has led to a growing concern over academic integrity, particularly in community colleges where students may lack the resources or support to navigate these challenges effectively. However, rather than viewing AI as a threat to academic honesty, educators can use this moment to redesign assessments that emphasize critical thinking, creativity, and problem-solving—skills that AI cannot easily replicate. For example, instead of traditional essays, instructors might assign reflective portfolios, oral presentations, or collaborative projects that require students to engage deeply with the material and demonstrate their understanding in ways that go beyond what AI can produce. Faculty development programs should focus on equipping instructors with the tools and strategies to design assessments that are both AI-resistant and pedagogically sound, ensuring that students are evaluated on their ability to think critically and apply knowledge, rather than their ability to generate text.

Practical Applications: Strategies for Faculty Development

To harness AI’s benefits while mitigating risks, community college faculty can adopt five evidence-based strategies, each enhancing teaching practice and student success:
1. AI as a Virtual Tutor: NLP models like ChatGPT offer 24/7 support. A developmental math instructor might direct students to ask, “Explain quadratic equations,” receiving tailored responses, freeing class time for conceptual exploration (Topol, 2019). Faculty can train on integrating such tools via workshops, enhancing their tutoring capacity.
1. Hybrid Course Design: AI platforms enable asynchronous lectures, reserving in-person sessions for interaction. A sociology instructor could post AI-generated summaries on social inequality, then facilitate debates, accommodating diverse schedules (Quality Matters, 2022). Faculty development can include HyFlex training to optimize this balance.
1. Creative Exploration: Tools like MidJourney foster innovation. An English instructor might have students generate visual interpretations of Their Eyes Were Watching God, sparking discussions on symbolism (Hutchison & Woodward, 2014). Workshops on AI-driven creativity can empower faculty to adapt this approach.
1. Personalized Feedback: AI tools like Grammarly flag errors, allowing instructors to refine feedback with comments like, “Expand this argument,” blending efficiency with mentorship (Pritts, 2023). Faculty can learn to leverage AI feedback systems through peer mentoring.
1. Redesigning Assessments for AI-Resistance: In light of the challenges posed by AI-generated content, faculty can adopt assessment strategies that prioritize critical thinking and creativity. For example, instead of traditional essays, instructors might assign reflective portfolios, oral presentations, or collaborative projects that require students to engage deeply with the material and demonstrate their understanding in ways that go beyond what AI can produce. Faculty development programs should focus on equipping instructors with the tools and strategies to design assessments that are both AI-resistant and pedagogically sound, ensuring that students are evaluated on their ability to think critically and apply knowledge, rather than their ability to generate text.
At Sacramento City College (SCC), a 2023 pilot integrated AI into developmental English courses. Faculty used ChatGPT as a virtual tutor, assigning students to draft essays and refine them with AI feedback before submission. Instructors then provided final critiques, focusing on argumentation over mechanics. A survey of 50 students found 78% felt more confident in writing, while faculty reported a 30% reduction in grading time (SCC Faculty Development Report, 2023, unpublished). However, 20% of students lacked reliable internet, prompting SCC to expand Wi-Fi access. This case underscores AI’s potential to enhance teaching efficiency and student outcomes, while highlighting equity challenges faculty developers must address.

In response to concerns about AI-generated content, SCC also piloted a new assessment model in which students were required to present their essays orally and engage in peer review sessions. This approach not only reduced the risk of AI-generated submissions but also fostered a deeper understanding of the material through active engagement and collaboration. Faculty reported that students were more invested in the learning process and demonstrated higher levels of critical thinking and creativity in their work.

Conclusion

AI’s evolution—from ancient myths to ChatGPT’s theatrical debut—mirrors humanity’s quest to extend its capabilities. In community colleges, it offers accessibility and efficiency, yet risks eroding the relational core of teaching. Faculty development can bridge this gap by equipping instructors with strategies to integrate AI as a tutor, design tool, creative aid, feedback enhancer, and discussion catalyst. The SCC case study illustrates this balance in practice. As Davidson (2021) and Pritts (2023) argue, the goal is to delegate routine tasks to AI while reserving inspiration and mentorship for faculty—a principle rooted in SoTL. By fostering AI literacy and ethical oversight, faculty developers can ensure AI amplifies, rather than supplants, the human element of education.

Moreover, as the Inside Higher Ed article highlights, the rise of AI-generated content presents an opportunity to rethink assessment practices. By designing assessments that emphasize critical thinking, creativity, and collaboration, faculty can ensure that students are evaluated on their ability to engage deeply with the material, rather than their ability to generate text. This shift not only addresses concerns about academic integrity but also prepares students for a future in which AI is an integral part of the workforce.

David E. Balch, PhD, is a professor at Rio Hondo College and has published articles in the areas of ethics, humor, distance education, and AI.

Robert Blanck, MA, has 35 years’ experience in teaching and administration within the fields of education and business. Blanck earned the Lean Six Sigma Black Belt designation for industrial quality control.

Note: this article used collaboration between the human authors and the AI program Grok (xAI).

References

Barnes & Noble Education. (2022, June 28). College 2030™ report: Higher education’s preparation post-pandemic. Business Wire. https://www.businesswire.com/news/home/20220628005207/en/

Baylor, A. L., & Ryu, J. (2003). The API model: Using AI to enhance learning. Educational Technology Research and Development, 51(4), 89–102. https://doi.org/10.1007/BF02504554

Buchanan, B. G., & Shortliffe, E. H. (1984). Rule-based expert systems: The MYCIN experiments of the Stanford Heuristic Programming Project. Addison-Wesley.

Čapek, K. (2004). R.U.R. (Rossum’s Universal Robots) (C. Novack, Trans.). Penguin Classics. (Original work published 1920)

Davidson, C. N. (2021). The new education: How to revolutionize the university to prepare students for a world in flux. Basic Books.

EDUCAUSE. (2023). AI and academic integrity: Rethinking assessment. https://www.educause.edu

Hutchison, A., & Woodward, L. (2014). A planning cycle for integrating digital technology into literacy instruction. The Reading Teacher, 67(6), 455–464. https://doi.org/10.1002/trtr.1225

McCarthy, J. (2007). What is artificial intelligence? Stanford University. http://jmc.stanford.edu/articles/whatisai/whatisai.pdf

Means, B., Bakia, M., & Murphy, R. (2020). Learning online: What research tells us about whether, when and how. Routledge.

Mitchell, T. M. (1997). Machine learning. McGraw-Hill.

O’Neil, C. (2017). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown. Note: changed the date from 2016 to 2017

Pritts, N. (2023). AI and the future of critical thinking in higher education. Journal of Educational Technology, 40(2), 123–135.

Quality Matters. (2022). 2022 higher education trends report. https://www.qualitymatters.org/research

Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T., & Hassabis, D. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484–489. https://doi.org/10.1038/nature16961

Spitale, G., Biller-Andorno, N., & Germani, F. (2023). AI model GPT-3 (dis)informs us better than humans. Science Advances, 9(5), Article eade7823. https://doi.org/10.1126/sciadv.ade7823

Topol, E. J. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Basic Books.

Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59(236), 433–460. https://doi.org/10.1093/mind/LIX.236.433

Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2
Source link
March 31, 2025
AI-Powered Teaching: Practical Tools for Community College Faculty – Faculty Focus
Artificial intelligence (AI) has transitioned from a speculative concept to a transformative tool in higher education, particularly within community colleges. This article explores AI’s historical evolution, critically assesses its benefits and challenges for teaching, and provides evidence-based strategies for faculty to integrate AI effectively. Drawing on the scholarship of teaching and learning (SoTL), it argues that AI can enhance accessibility and efficiency while preserving the human essence of education. A case study from a California community college illustrates these principles in action. Faculty developers and instructors can use this framework to harness AI’s potential, ensuring it supports rather than supplants their pedagogical roles.

Introduction

In Ayad Akhtar’s 2023 play McNeal, ChatGPT performs alongside Robert Downey Jr., a theatrical milestone reflecting artificial intelligence’s (AI) encroachment into domains once deemed uniquely human (Akhtar, 2023, as cited in “From Čapek to ChatGPT”). This echoes Karel Čapek’s R.U.R. (Rossum’s Universal Robots), where artificial beings, created to serve, ultimately undo their creators (Čapek, 1920/2004). A century later, AI is no longer fiction but a dynamic force in community college education, reshaping how faculty teach, and students learn. For instructors serving diverse, often underserved populations, AI raises critical questions: How has it evolved into a pedagogical tool? What are its merits and limitations? How can it enhance teaching without diminishing faculty agency?

This article addresses these questions through a three-part exploration: tracing AI’s historical development, critically evaluating its role in community college classrooms, and offering practical, evidence-based strategies for faculty. Grounded in the scholarship of teaching and learning (SoTL), it expands on prior work (e.g., Davidson, 2021; Pritts, 2023) to position AI as a partner in faculty development. A case study illustrates these principles in practice, aligning with Faculty Focus’s mission to improve teaching and student success in higher education.

Historical Context: AI’s Evolution and Educational Implications

AI’s journey from myth to modernity informs its current educational applications. Ancient tales of Talos, a bronze automaton guarding Europa, and Pygmalion’s animated sculpture reveal humanity’s early fascination with artificial life (Hamilton, 1942). By the 3rd century BC, Greek inventors like Archytas built the steam-powered Flying Pigeon, an early self-operating device (Berryman, 2003). The 18th-century Jaquet-Droz automata—mechanical dolls mimicking human actions—furthered this vision (Riskin, 2016). Philosophers like Descartes and Leibniz speculated on machine cognition, while 19th-century pioneers Babbage and Lovelace laid computing’s foundations (Copeland, 2012).

The modern AI field emerged in 1950 when Alan Turing posed, “Can machines think?” (Turing, 1950), followed by John McCarthy’s naming of “artificial intelligence” (McCarthy, 2007). Early systems like MYCIN, which diagnosed infections using rule-based logic, showcased potential but lacked adaptability (Buchanan & Shortliffe, 1984). The 1990s introduced machine learning, enabling AI to learn from data—email spam filters improved iteratively, marking a shift from static programming (Mitchell, 1997). The 2010s brought deep learning, with neural networks emulating human cognition; Google’s AlphaGo defeated Go champion Lee Sedol in 2016, devising strategies its creators couldn’t predict (Silver et al., 2016).

Since 2020, generative AI—exemplified by OpenAI’s GPT-3 and ChatGPT—has produced human-like text, transforming fields from healthcare to education (Spitale et al., 2023; Topol, 2019). In classrooms, AI’s trajectory is evident: 1980s grading software evolved into 2020s virtual tutors, shifting from peripheral aids to central tools (Baylor & Ryu, 2003). For community college faculty, this history contextualizes AI’s rapid ascent, highlighting its capacity to augment teaching—yet it also underscores the need for critical oversight, a theme explored next.

AI’s integration into community colleges offers transformative potential but demands scrutiny, particularly given the sector’s diverse student body—often first-generation, working adults, or developmental learners (Cohen et al., 2014).

Benefits: Accessibility and Faculty Efficiency
AI enhances accessibility, a cornerstone of community college missions. Hybrid and HyFlex models, supported by AI-driven platforms like Zoom, allow asynchronous learning, vital for students balancing work and family (Quality Matters, 2022). A 2022 report documented a 220% surge in preference for online/hybrid courses (from 9% in 2020 to 29% in 2022), reflecting AI’s role in broadening access (Kelly, 2022, as cited in Quality Matters, 2022). Faculty benefit too: automated grading reduces routine burdens, while NLP tools like ChatGPT provide instant student support, amplifying instructors’ reach (Barnes & Noble Education, 2022; Topol, 2019).

Challenges: Engagement and Skill Development
However, AI’s efficacy hinges on student self-regulation, a persistent challenge where developmental courses predominate (Zimmerman, 2002). Virtual settings often lack in-person dynamics—non-verbal cues, spontaneous discussions—potentially weakening engagement (Quality Matters, 2022). Faculty unease is notable: 54% prefer fully in-person instruction, citing discomfort with digital mediation (Barnes & Noble Education, 2022). Overreliance on AI risks undermining critical thinking; Pritts (2023) warns that tools generating papers may erode analytical skills, echoing SoTL calls for active learning (Davidson, 2021).

Ethical Considerations: Equity and Agency
Ethical dilemmas loom large. Algorithmic bias in grading systems can exacerbate inequities, disproportionately affecting marginalized learners (O’Neil, 2016). Access disparities—unreliable internet or devices—threaten equitable implementation (Means et al., 2020). Spitale et al. (2023) highlight AI’s dual edge: it clarifies concepts but also crafts compelling disinformation, posing risks to academic integrity. Without oversight, AI could shift from facilitator to arbiter, diminishing faculty agency—a concern rooted in Čapek’s cautionary tale (1920/2004). Faculty development must address these tensions to ensure AI serves educational goals.

AI and Academic Integrity: Redrawing Assessment
The rise of AI tools like ChatGPT has forced educators to rethink traditional assessment methods. As discussed in Inside Higher Ed (2025), the prevalence of AI-generated content has made it increasingly difficult to distinguish between student work and machine-generated output. This has led to a growing concern over academic integrity, particularly in community colleges where students may lack the resources or support to navigate these challenges effectively. However, rather than viewing AI as a threat to academic honesty, educators can use this moment to redesign assessments that emphasize critical thinking, creativity, and problem-solving—skills that AI cannot easily replicate. For example, instead of traditional essays, instructors might assign reflective portfolios, oral presentations, or collaborative projects that require students to engage deeply with the material and demonstrate their understanding in ways that go beyond what AI can produce. Faculty development programs should focus on equipping instructors with the tools and strategies to design assessments that are both AI-resistant and pedagogically sound, ensuring that students are evaluated on their ability to think critically and apply knowledge, rather than their ability to generate text.

Practical Applications: Strategies for Faculty Development

To harness AI’s benefits while mitigating risks, community college faculty can adopt five evidence-based strategies, each enhancing teaching practice and student success:
1. AI as a Virtual Tutor: NLP models like ChatGPT offer 24/7 support. A developmental math instructor might direct students to ask, “Explain quadratic equations,” receiving tailored responses, freeing class time for conceptual exploration (Topol, 2019). Faculty can train on integrating such tools via workshops, enhancing their tutoring capacity.
1. Hybrid Course Design: AI platforms enable asynchronous lectures, reserving in-person sessions for interaction. A sociology instructor could post AI-generated summaries on social inequality, then facilitate debates, accommodating diverse schedules (Quality Matters, 2022). Faculty development can include HyFlex training to optimize this balance.
1. Creative Exploration: Tools like MidJourney foster innovation. An English instructor might have students generate visual interpretations of Their Eyes Were Watching God, sparking discussions on symbolism (Hutchison & Woodward, 2014). Workshops on AI-driven creativity can empower faculty to adapt this approach.
1. Personalized Feedback: AI tools like Grammarly flag errors, allowing instructors to refine feedback with comments like, “Expand this argument,” blending efficiency with mentorship (Pritts, 2023). Faculty can learn to leverage AI feedback systems through peer mentoring.
1. Redesigning Assessments for AI-Resistance: In light of the challenges posed by AI-generated content, faculty can adopt assessment strategies that prioritize critical thinking and creativity. For example, instead of traditional essays, instructors might assign reflective portfolios, oral presentations, or collaborative projects that require students to engage deeply with the material and demonstrate their understanding in ways that go beyond what AI can produce. Faculty development programs should focus on equipping instructors with the tools and strategies to design assessments that are both AI-resistant and pedagogically sound, ensuring that students are evaluated on their ability to think critically and apply knowledge, rather than their ability to generate text.
At Sacramento City College (SCC), a 2023 pilot integrated AI into developmental English courses. Faculty used ChatGPT as a virtual tutor, assigning students to draft essays and refine them with AI feedback before submission. Instructors then provided final critiques, focusing on argumentation over mechanics. A survey of 50 students found 78% felt more confident in writing, while faculty reported a 30% reduction in grading time (SCC Faculty Development Report, 2023, unpublished). However, 20% of students lacked reliable internet, prompting SCC to expand Wi-Fi access. This case underscores AI’s potential to enhance teaching efficiency and student outcomes, while highlighting equity challenges faculty developers must address.

In response to concerns about AI-generated content, SCC also piloted a new assessment model in which students were required to present their essays orally and engage in peer review sessions. This approach not only reduced the risk of AI-generated submissions but also fostered a deeper understanding of the material through active engagement and collaboration. Faculty reported that students were more invested in the learning process and demonstrated higher levels of critical thinking and creativity in their work.

Conclusion

AI’s evolution—from ancient myths to ChatGPT’s theatrical debut—mirrors humanity’s quest to extend its capabilities. In community colleges, it offers accessibility and efficiency, yet risks eroding the relational core of teaching. Faculty development can bridge this gap by equipping instructors with strategies to integrate AI as a tutor, design tool, creative aid, feedback enhancer, and discussion catalyst. The SCC case study illustrates this balance in practice. As Davidson (2021) and Pritts (2023) argue, the goal is to delegate routine tasks to AI while reserving inspiration and mentorship for faculty—a principle rooted in SoTL. By fostering AI literacy and ethical oversight, faculty developers can ensure AI amplifies, rather than supplants, the human element of education.

Moreover, as the Inside Higher Ed article highlights, the rise of AI-generated content presents an opportunity to rethink assessment practices. By designing assessments that emphasize critical thinking, creativity, and collaboration, faculty can ensure that students are evaluated on their ability to engage deeply with the material, rather than their ability to generate text. This shift not only addresses concerns about academic integrity but also prepares students for a future in which AI is an integral part of the workforce.

David E. Balch, PhD, is a professor at Rio Hondo College and has published articles in the areas of ethics, humor, distance education, and AI.

Robert Blanck, MA, has 35 years’ experience in teaching and administration within the fields of education and business. Blanck earned the Lean Six Sigma Black Belt designation for industrial quality control.

Note: this article used collaboration between the human authors and the AI program Grok (xAI).

References

Barnes & Noble Education. (2022, June 28). College 2030™ report: Higher education’s preparation post-pandemic. Business Wire. https://www.businesswire.com/news/home/20220628005207/en/

Baylor, A. L., & Ryu, J. (2003). The API model: Using AI to enhance learning. Educational Technology Research and Development, 51(4), 89–102. https://doi.org/10.1007/BF02504554

Buchanan, B. G., & Shortliffe, E. H. (1984). Rule-based expert systems: The MYCIN experiments of the Stanford Heuristic Programming Project. Addison-Wesley.

Čapek, K. (2004). R.U.R. (Rossum’s Universal Robots) (C. Novack, Trans.). Penguin Classics. (Original work published 1920)

Davidson, C. N. (2021). The new education: How to revolutionize the university to prepare students for a world in flux. Basic Books.

EDUCAUSE. (2023). AI and academic integrity: Rethinking assessment. https://www.educause.edu

Hutchison, A., & Woodward, L. (2014). A planning cycle for integrating digital technology into literacy instruction. The Reading Teacher, 67(6), 455–464. https://doi.org/10.1002/trtr.1225

McCarthy, J. (2007). What is artificial intelligence? Stanford University. http://jmc.stanford.edu/articles/whatisai/whatisai.pdf

Means, B., Bakia, M., & Murphy, R. (2020). Learning online: What research tells us about whether, when and how. Routledge.

Mitchell, T. M. (1997). Machine learning. McGraw-Hill.

O’Neil, C. (2017). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown. Note: changed the date from 2016 to 2017

Pritts, N. (2023). AI and the future of critical thinking in higher education. Journal of Educational Technology, 40(2), 123–135.

Quality Matters. (2022). 2022 higher education trends report. https://www.qualitymatters.org/research

Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T., & Hassabis, D. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484–489. https://doi.org/10.1038/nature16961

Spitale, G., Biller-Andorno, N., & Germani, F. (2023). AI model GPT-3 (dis)informs us better than humans. Science Advances, 9(5), Article eade7823. https://doi.org/10.1126/sciadv.ade7823

Topol, E. J. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Basic Books.

Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59(236), 433–460. https://doi.org/10.1093/mind/LIX.236.433

Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2
Source link
March 31, 2025
Due Dates Provide a Structure for Spaced Learning – Faculty Focus

Every semester, a student hoping to “earn their desired grade” approaches me at the last minute, asking to make up all their missed assignments. I have a standard missed due date policy, 1) let me know (communication) and 2) get it done in a week (extended deadline). The students requesting to stockpile assignments at the end of the semester usually had not completed the assignment at all or had not completed the assignment even with an extended deadline. Basically, they hadn’t even tried to complete assignments. It wasn’t like they had completed it at 12:01 am and it was due at 11:59 pm.

That being said, I do wonder, Why not grant a student’s request to get it (the class) all done at once? You know the requests: “I know this already. Isn’t there a test I can take so I don’t have to sit through the class?” or “I am sure I can get all these assignments done before finals week.” It makes sense that if a student can pass all the assessments, they should pass the class. So why not just complete all the assessments at the same time?

Would these students who were absent from most of the class or hadn’t done any of the assignments on their due dates pass the class with a C or better? Probably. So why enforce due dates at all. Why shouldn’t a professor simply allow a student to complete all the assignments before final grades are due without regard to timing?

The caveat in this “all at once” scenario is that a successful grade is wrongly associated with learning. If a student completes all the assignments in a few days during the semester, then assessments are the sole criteria used to define successful learning. However, the truth is that learning means much more than successfully navigating a certain percentage of the assignments.

In my reality, most students could use their previous knowledge to muddle through the assessments of an introductory course at an accelerated pace. Let’s view the situation from the perspective of a student. They have after all been in school for the last 13 years and they have become really good at taking quizzes and exams and writing essays with thesis statements. My pre- and post-quiz data for the course reveals this. At the beginning of the semester without warning, I ask a group of anxious, ill-prepared students to (within a limited and unreasonable amount of time) answer 20 questions about a topic that they haven’t even considered before walking into the room. The questions are a sampling of the final exam. The average on the pre-quiz hovers at about 50%. Given more time and a couple of days to study and contemplate, most of these students would pass the pre-quiz, and in essence, have the knowledge required to pass the final exam.

Nevertheless, is completing all the assessments what defines learning the contents of a course? Should a student be allowed to stockpile all the assignments and get credit for them as long as they get them done before the final grades are due. In my humble opinion, absolutely not, and it’s not for the classic soft skills reason. Of course, deadlines help students to navigate the world, (e.g. being responsible). After all, the professor is required to turn in final grades by a deadline.

That said, to me, deadlines are important because people (not just students) learn best when information is spaced out or interleaved (Birnbaum et al., 2013; Samani and Pan, 2021). In order to retrieve something from memory, you need time to learn it. Retrieving information is when you know something without having to look it up—like your name and phone number or all the lyrics of your favorite song. This type of learning requires time—spaced learning. You need to learn it, forget it, be asked about it, refresh it, forget it, be asked about it, refresh it and then if you’re lucky you can retrieve it (Feng et al., 2019; Kobayashi, 2022). This is considered spaced learning and is why deadlines are so important.

Due dates provide the structure for spaced learning and learning that is more substantive. Much to the chagrin of the student who wants to “get it all done at once,” this isn’t the way to learn retrievable material. Retrieving information is what allows you to progress to the next set of information and then progress to creativity and eventually creation. Assessments play a crucial role, but true learning doesn’t come from merely completing them. Instead, it happens through active participation in class, where students engage with the material and are consistently challenged by the professor to recall and apply previous lessons.

As professors, we should enforce due dates to give students the time to learn content in a meaningful and retrievable way. But maybe, just maybe, we can do it with a spoon full of sugar.

Adriana J. LaGier is an American Cell Biologist, whose main area of research is active learning and mechanobiology. She is a professor of biology at Grand View University in Des Moines, Iowa, specializing in teaching undergraduate level general biology and cell biology courses.

References

Birnbaum, Monica S., Nate Kornell, Elizabeth Ligon Bjork, and Robert A. Bjork. 2013. “Why Interleaving Enhances Inductive Learning: The Roles of Discrimination and Retrieval.” Memory & Cognition 41 (3): 392–402. https://doi.org/10.3758/s13421-012-0272-7.

Feng, Kanyin, Xiao Zhao, Jing Liu, Ying Cai, Zhifang Ye, Chuansheng Chen, and Gui Xue. 2019. “Spaced Learning Enhances Episodic Memory by Increasing Neural Pattern Similarity Across Repetitions.” The Journal of Neuroscience 39 (27): 5351–60. https://doi.org/10.1523/JNeurosci.2741-18.2019.

Kobayashi, Keiichi. 2022. “The Retrieval Practice Hypothesis in Research on Learning by Teaching: Current Status and Challenges.” Frontiers in Psychology 13 (May):842668. https://doi.org/10.3389/fpsyg.2022.842668.

Samani, Joshua, and Steven C. Pan. 2021. “Interleaved Practice Enhances Memory and Problem-Solving Ability in Undergraduate Physics.” NPJ Science of Learning 6 (November):32. https://doi.org/10.1038/s41539-021-00110-x.

Source link

March 28, 2025
Due Dates Provide a Structure for Spaced Learning – Faculty Focus

Every semester, a student hoping to “earn their desired grade” approaches me at the last minute, asking to make up all their missed assignments. I have a standard missed due date policy, 1) let me know (communication) and 2) get it done in a week (extended deadline). The students requesting to stockpile assignments at the end of the semester usually had not completed the assignment at all or had not completed the assignment even with an extended deadline. Basically, they hadn’t even tried to complete assignments. It wasn’t like they had completed it at 12:01 am and it was due at 11:59 pm.

That being said, I do wonder, Why not grant a student’s request to get it (the class) all done at once? You know the requests: “I know this already. Isn’t there a test I can take so I don’t have to sit through the class?” or “I am sure I can get all these assignments done before finals week.” It makes sense that if a student can pass all the assessments, they should pass the class. So why not just complete all the assessments at the same time?

Would these students who were absent from most of the class or hadn’t done any of the assignments on their due dates pass the class with a C or better? Probably. So why enforce due dates at all. Why shouldn’t a professor simply allow a student to complete all the assignments before final grades are due without regard to timing?

The caveat in this “all at once” scenario is that a successful grade is wrongly associated with learning. If a student completes all the assignments in a few days during the semester, then assessments are the sole criteria used to define successful learning. However, the truth is that learning means much more than successfully navigating a certain percentage of the assignments.

In my reality, most students could use their previous knowledge to muddle through the assessments of an introductory course at an accelerated pace. Let’s view the situation from the perspective of a student. They have after all been in school for the last 13 years and they have become really good at taking quizzes and exams and writing essays with thesis statements. My pre- and post-quiz data for the course reveals this. At the beginning of the semester without warning, I ask a group of anxious, ill-prepared students to (within a limited and unreasonable amount of time) answer 20 questions about a topic that they haven’t even considered before walking into the room. The questions are a sampling of the final exam. The average on the pre-quiz hovers at about 50%. Given more time and a couple of days to study and contemplate, most of these students would pass the pre-quiz, and in essence, have the knowledge required to pass the final exam.

Nevertheless, is completing all the assessments what defines learning the contents of a course? Should a student be allowed to stockpile all the assignments and get credit for them as long as they get them done before the final grades are due. In my humble opinion, absolutely not, and it’s not for the classic soft skills reason. Of course, deadlines help students to navigate the world, (e.g. being responsible). After all, the professor is required to turn in final grades by a deadline.

That said, to me, deadlines are important because people (not just students) learn best when information is spaced out or interleaved (Birnbaum et al., 2013; Samani and Pan, 2021). In order to retrieve something from memory, you need time to learn it. Retrieving information is when you know something without having to look it up—like your name and phone number or all the lyrics of your favorite song. This type of learning requires time—spaced learning. You need to learn it, forget it, be asked about it, refresh it, forget it, be asked about it, refresh it and then if you’re lucky you can retrieve it (Feng et al., 2019; Kobayashi, 2022). This is considered spaced learning and is why deadlines are so important.

Due dates provide the structure for spaced learning and learning that is more substantive. Much to the chagrin of the student who wants to “get it all done at once,” this isn’t the way to learn retrievable material. Retrieving information is what allows you to progress to the next set of information and then progress to creativity and eventually creation. Assessments play a crucial role, but true learning doesn’t come from merely completing them. Instead, it happens through active participation in class, where students engage with the material and are consistently challenged by the professor to recall and apply previous lessons.

As professors, we should enforce due dates to give students the time to learn content in a meaningful and retrievable way. But maybe, just maybe, we can do it with a spoon full of sugar.

Adriana J. LaGier is an American Cell Biologist, whose main area of research is active learning and mechanobiology. She is a professor of biology at Grand View University in Des Moines, Iowa, specializing in teaching undergraduate level general biology and cell biology courses.

References

Birnbaum, Monica S., Nate Kornell, Elizabeth Ligon Bjork, and Robert A. Bjork. 2013. “Why Interleaving Enhances Inductive Learning: The Roles of Discrimination and Retrieval.” Memory & Cognition 41 (3): 392–402. https://doi.org/10.3758/s13421-012-0272-7.

Feng, Kanyin, Xiao Zhao, Jing Liu, Ying Cai, Zhifang Ye, Chuansheng Chen, and Gui Xue. 2019. “Spaced Learning Enhances Episodic Memory by Increasing Neural Pattern Similarity Across Repetitions.” The Journal of Neuroscience 39 (27): 5351–60. https://doi.org/10.1523/JNeurosci.2741-18.2019.

Kobayashi, Keiichi. 2022. “The Retrieval Practice Hypothesis in Research on Learning by Teaching: Current Status and Challenges.” Frontiers in Psychology 13 (May):842668. https://doi.org/10.3389/fpsyg.2022.842668.

Samani, Joshua, and Steven C. Pan. 2021. “Interleaved Practice Enhances Memory and Problem-Solving Ability in Undergraduate Physics.” NPJ Science of Learning 6 (November):32. https://doi.org/10.1038/s41539-021-00110-x.

Source link

March 28, 2025