TORONTO – June 7, 2024 – Top Hat, the leader in student engagement solutions for higher education, is proud to announce that four exceptional First Nations students have been awarded this year’s Shannen’s Dream Scholarship. Launched in 2022 by the First Nations Child & Family Caring Society with the support of Top Hat and the Collure Family of Richmond Hill, ON, each recipient will receive $10,000 to support their pursuit of a post-secondary education.
“We are truly inspired by this year’s Shannen’s Dream Scholarship recipients, both in terms of their academic achievements and as volunteers and agents of change within their communities,” said Maggie Leen, CEO of Top Hat. “As future leaders, doctors, scientists, and educators, they exemplify what’s possible when dedicated individuals have access to the benefits of higher education.”
The Scholarship is named in honor of Shannen Koostachin, a courageous young leader from Attawapiskat First Nation who inspired a national movement to establish safe and comfortable schools for First Nations students. What makes the Shannen’s Dream Scholarship particularly special is the ‘pay-it-forward’ component, which requires recipients to make a measurable contribution to the Shannen’s Dream campaign or related First Nations initiative.
“Our scholarship recipients are honoring Shannen’s legacy through their leadership, their community contributions and their academic achievements,” said Cindy Blackstock, Executive Director of the Caring Society. “We are grateful to Top Hat and the Collure Family for their support and for sharing our conviction that a more equitable and just society rests on ensuring First Nations students are able to pursue their dreams of a high quality education.”
Meet the 2024 Shannen’s Dream Scholarship Recipients
Aleria McKay was raised on Six Nations of the Grand River and is completing her Bachelor of Education at York’s Waaban Indigenous Teacher Education Program. A poet, playwright and educator, this fall she will start her Masters of Fine Arts in Creative Writing at the University of British Columbia.
Jaimey Jacobs is Ojibwe and a band member of the Walpole Island First Nation. A first year medical student at the Schulich School of Medicine and Dentistry at Western University, Jaimey is a passionate advocate for Indigenous healthcare and supporting Indigenous youth in navigating educational opportunities within the healthcare profession.
Rainbird Daniels is Plains Cree, Yankton Sioux, and Dakota from the Sturgeon Lake First Nation. She is pursuing a degree in Psychology at York University in Toronto where she also serves as the President of the Indigenous Student Association. As an Indigenous Languages Specialist at the Centre for Indigenous Knowledge and Languages, she is deeply committed to promoting cultural awareness and advancing human rights.
Taylor Nicholls is from the Wahnapitae First Nation and is pursuing a Master’s of Science in Biology at Laurentian University. Her thesis involves assessing various environmental contaminants in fish the Wahnapitae First Nation relies on as a traditional food source. Taylor is an ardent environmentalist whose research involves weaving Western science, citizen science, and traditional ecological knowledge.
About Shannen’s Dream Scholarship
The Shannen’s Dream Scholarship was established to assist First Nations youth with the financial burdens of post-secondary education. The scholarship honors Shannen Koostachin, whose advocacy for safe and comfortable schools for First Nations students ignited a nationwide movement. This scholarship aims to continue her legacy by empowering First Nations students to achieve their educational aspirations. To learn more, please visit www.fncaringsociety.com.
About Top Hat
As the leader in student engagement solutions for higher education, Top Hat enables educators to employ evidence-based teaching practices through interactive content, tools, and activities in in-person, online and hybrid classroom environments. Thousands of faculty at 750 leading North American colleges and universities use Top Hat to create meaningful, engaging and accessible learning experiences for students before, during, and after class. To learn more, please visit tophat.com.
This will be the second part of a series of blogposts about how to use IPEDS, The Integrated Postsecondary Education Data System of the federal government. If you’re just starting, I highly recommend you go to the first post to bring yourself up to speed on the basics. If you don’t, some of this might not make sense.
In that post, I covered several of the ways you can extract simple tables of data for a single year or a single institution; or summary data, including fairly basic and interactive charts when you’re looking for something simple. In this one, I’ll go over how to extract custom data over multiple years, and then walk you through the frustrating process of making sense of the output. Warning: I get a bit cranky about this, because the data formats are largely unchanged since I started doing this perhaps 20 years ago, and they create far more work for the end user than they should.
The last post covered the options in italics. This one will cover the options in bold.
Data Explorer Publications and Products Data Trends Look Up an Institution Statistical Tables Data Feedback Report Summary Tables
Custom Data Files Compare Institutions Complete Data Files Access Database
Custom Data files is a fairly easy way to get the data you want for a single year. In this example, I’ve used EZ Group to select all institutions (again, larger selections are better because it’s easy to remove but harder to add), and clicked “Institutions” and then the “Select All” button. It looks like this.
Click the “Continue” tab, and choose “csv” as your download option.
Click on the file that you just downloaded to open it (it should open with Excel), and you’ll see something like this (not all columns are displayed due to space constraints.)
If you want to do this for multiple years, you can go back, change the year, and repeat the download and then stack the files. The real advantage of this approach is that the data in the columns come in as labels: That is, the size categories are listed as “Under 1,000” or “5,000-9,999” for instance. That means, especially with large files, you don’t have to translate codes, where “Under 1,000” is listed as 1, and “1,000 to 5,000” is listed as 2, etc. This keeps you from doing multiple LOOKUP functions in Excel that are a part of other formats.
If you are going to do a lot of work in IPEDS, I highly recommend you use this method to create one giant file of institutional characteristics to import into an Access Database, and use it to merge that with data from statistical downloads (like endowments, admissions, financial aid, etc.) There are a couple of reasons for this: It’s too easy to overlook or skip a variable you want to include in your subsequent downloads, but more important, IPEDS only allows 250 variables in a single file, so this can save you 70 or 100 or 150 spots in the future.
You’ll notice that there are also options to download this data in STATA, SPSS, or SAS, which are statistical programs. Those require downloading a csv file, a script for the software, and then editing the script to point to your file, running it and saving the output. With the Custom Data Files option, that’s a bit superfluous.
Despite the confusion and difficult work arounds that are native to the Compare Institutions option, it’s the one I use most often. Warning: This is not for people who do not have Sitzfleisch. Even the best data cleaning tools are stymied by some of the quirks in IPEDS.
So let’s go back to our institutional selections, and select all in the IPEDS universe. If you want to start with things like Carnegie Classifications because you ignored my earlier advice, we can do that, and then we’ll look at Fall Enrollment over time. Let’s start with the former. I’d recommend selecting it for one year, unless you want to look at how those classifications have changed over time. We do that like this.
Then (and this is where it gets tricky), we’ll start specifying enrollment variables. Let’s say we want to look at how enrollment has changed over a span of time, so we’ll use Fall, 2022 and Fall, 2012. You have a lot of options, but only these options (IPEDS really should allow you to query the database in the ways you want, but that’s another story.)
Let’s do the first option: By Gender, Status, and level. We’ll have the chance to look at men and women (IPEDS has reported gender as binary as its policy, not mine), full- or part-time, status, and graduate or undergraduate level.
Here’s how that selection is done.
When you approve that, this is what you see. Note that this selection creates 17 variables in your data output: One for the Carnegie Classification, and 16 for the enrollment data. If you added another year, you’d add eight more, and so on. If you got more granular on the enrollment data, it would increase those counts as well.
Approve the selections (these are the ones I use, but you can change them. I highly recommend including UnitID unless you’re doing a short, quick analysis).
You will get a ZIP file, with the raw data and the value labels. In this case, the only values that need to be translated into labels are the Carnegie Classifications. That translator table looks like this. If you are proficient in Excel, it’s not hard to use a VLOOKUP or XLOOKUP function to translate those values into labels, but it’s still, IMHO, a quirk leftover from days when it made sense to keep file size as small as possible.
The actual data file looks like this, and it’s probably the thing that makes a lot of people decide to never do IPEDS again. The first column contains the ID number, the second contains the name, the third contains the numeric value of the Carnegie classification, and the fourth? Well, the fourth variable is a tricky one, as it’s actually four variables rolled into one: Year, level (in this case undergraduate), gender, and status (in this case, full-time.)
For this to be most useful, the data should look like this, with one row for every discrete combination of characteristics:
And that’s the hard part: I use a Tableau Data Restructuring Tool, Excel tools like Flash Fill (if you don’t know it, you have to check it out), Excel Add-ins, Kutools for Excel, EasyMorph and Able Bits. I’ve used Tableau Prep, but frankly find it confusing and often frustrating.
Getting your data into this format not only makes it easier to visualize in Tableau, but it also helps you create better pivot tables for the spreadsheet lovers in your office.
There are two other options in IPEDS, the Access Database and Complete Files.
Complete Files is easy, because you can download with one click the complete survey (admissions, financial aid, degrees awarded, etc.) But again, you get those pesky codes you need to translate, and no translator files or even the ability to translate variable names. It’s a major pain. If you’re going to go this route, I’d recommend the SPSS, STATA, or SAS options, where the script will translate and output the file for you. Another (IMHO) unnecessary step. IPEDS could make this much easier.
And, to top it off, if you download the enrollment file, for instance, the values are not discreet. You’ll have one column for total, which is the sum of men and women separately. That same total will roll up full- and part-time. It will roll up grads and undergrads. You have to be very careful to break them apart and not double count everything.
Finally, I’ve tried and failed several times to make sense of the full Access Data Base option. It’s huge, it’s clunky, it’s in code, and it duplicates values: In short, it’s the worst of all available options, in a harder-to-use format. Enter at your own risk.
I hope these two posts have been helpful to you as you think about navigating IPEDS. And I hope someone at IPEDS reads this and realizes how much modernization could be brought to these important data.
Most, but not all, of the data visualizations on this site use data from IPEDS, the Integrated Postsecondary Education Data System. And all of the visualizations (as I recall) use Tableau, a very powerful data visualization tool, especially for people like me who don’t know how to write the code necessary in some software packages.
In this post, I’ll start with a few of the easiest and quickest ways to get data out of IPEDS. I’ll follow it up with one that dives a little deeper for people who like the raw data for analysis.
The question I get asked most often is how I get the information out of IPEDS. And that’s not an easy thing to answer, as I use several of the methods available depending on what I’m doing. Since you federal tax dollars have not yet been used to create an easy guide to IPEDS, I’m going to give you a primer on how to do the most simple things, and hope you’ll do like I did, which is to learn it the hard way through trial and error once you get started.
Some tips before we start: You can use Excel to get the information you want, but it ends up being a lot easier if you start with a single download of institutional characteristics in a table and load it to an Access database, if you’re even marginally proficient in that software. But for now I’ll presume you’re not.
In the IPEDS Data Center you’ll find several different ways to get IPEDS data The ones in bold will be covered here:
Data Explorer
Publications and Products
Data Trends
Look Up an Institution
Statistical Tables
Data Feedback Report
Summary Tables
Custom Data Files
Compare Institutions
Complete Data Files
Access Database
Data Explorer has aggregated data in a report, and it’s useful if you want to look up something quickly and if there is already a report that summarizes that information. It’s aggregate, so best for high level trends. For instance, if you look at Degrees Awarded by Ethnicity, you’ll see this. Note that you can change the year displayed, and download the Excel file.
Publications and Products can be helpful, but you may end up going down a rabbit hole chasing what you want, only to find it’s in a restricted file only available to researchers. You can find links to things like The Condition of Education or the Digest of Education Statistics which is a data rich treasure trove of information, mostly designed to print ala 1998; if you want to analyze it, you have a LOT of data clean up to do.
Data Trends shows data over time, and it can be very helpful if you want to look at a single statistic in a time sequence. Click on one of the questions and you’ll get your answer quickly. You can filter and download the data if you wish.
Look Up an Institution allows you to select any single college or university and look at almost all of the information it reports to IPEDS in one place. It can be helpful when you want to look up a few facts about an institution quickly, but otherwise I find little value in it.
Typing more of the name of the institution gets you easier results. For instance, you’ll get a long list if you just type “California.”
But as you type, the list gets shorter.
After you make your selection, you’ll get this, and you can click on the plus sign on the blue bars to expand.
Statistical Tables are less helpful for my work, but maybe they’ll be good for you. This is where you’ll get your first chance to select a group of colleges, so I’ll go over that first. You can choose almost any combination of institutions, by location, type, sector, or almost any variable.
I like to us EZ Group and make a large selection: It’s a lot easier to start with a large file and eliminate institutions than to try to augment it last. But if you are certain you want a set of four-year public institutions in California that admit freshmen, for instance, you can get that like this. The dialog box tells you you’ve selected 48 institutions.
In this case, you might want to look at total fall 2022 enrollment of undergraduates, in which case you’d select like this:
Keep clicking “Continue” until you get here, and specify the statistics you want.
And you’ll get something like this.
Data Feedback Report is mostly helpful for college and university staff looking at their own numbers in comparison to self-identified competitor or aspiration institutions. CHE did a story on this, and you can read a few articles a month there if you give them your email name (however, if you work in higher ed, you really should subscribe anyway.)
Summary Tables are very helpful for the casual user. Specify the variable you want to look at (in this case it’s enrollment by race and gender) and you’ll get a nice summary table over time.
However, you can also get a summary of the institutions you selected (if they’re still in memory) like this:
OR (this is the cool part) you can show individual data for a pre-selected set, or one you specify.
Go ahead and practice getting information out of IPEDS like this. You cannot break anything. There is a Start Over button in case you get stuck.
Good luck and check back soon to get the guide about the more powerful ways to extract information, coming soon.
If you are working for a higher education institution or a non-profit organization, you have probably utilized Google Jamboard as part of your teaching or professional development workshops.
Well my friends, our beloved software has reached the end of its life at Google. Now, we will need to find a suitable alternative to collaborate with others at our organization and to brainstorm ideas online.
Speaking of alternatives, here are some alternatives to Jamboard:
1) LucidSpark
2) Padlet
3) Figma
4) Zoom Whiteboard
5) Canva Whiteboard
Which one of these are your favorite? Let us know which one you’ve utilized before.
Dr. Jennifer T. Edwards Professor of Communication
Executive Director: International Artificial Intelligence and Communication Institute, Texas Social Media Research Institute, & Rural Communication Institute
My Social Media Channels! Remember to Follow Me on Twitter! @drjtedwards Subscribe to My Channel – YouTube Engage with Me on Facebook! Email Me! I am PR Friendly! – [email protected]
Universal Design for Learning (UDL) is a framework that involves using a variety of teaching methods to respond to the needs of all your higher ed students. It is a teaching technique that provides flexibility in how instructional materials are delivered—and is ideal for supporting students of all backgrounds, cultures and abilities.
Universal Design for Learning (UDL) helps college and university educators create flexible programs that are easy to adapt to the unique needs of all students. This teaching framework involves using many techniques—in everything from course delivery to media use—to reduce barriers and reach every college student.1
UDL guidelines are put into practice in a variety of facets, including outside of academia. For instance, you may use closed captioning when watching television. Your smartphone can read the information on a website aloud. Meanwhile, automatic doors make grocery stores more accessible. In these three scenarios, UDL helps level the playing field for everyone, regardless of ability.
The UDL guidelines are presented as a graphic organizer, or a matrix table. Vertically, the principles are grouped under engagement, representation, and action and expression. Horizontally, the UDL principles are presented as access, build and internalize. Each guideline has corresponding checkpoints that include best practices. The graphic organizer helps viewers understand how criteria differ across the three principles. View the table below for a breakdown of the UDL guidelines.
Provide multiple means of engagement
Provide multiple means of representation
Provide multiple means of action and expression
Recruiting interest
Perception
Physical action
Sustaining effort and persistence
Language and symbols
Expression and communication
Self-regulation
Comprehension
Executive function
2. What are the three core tenets of Universal Design for Learning?2
Action and expression: This learning approach involves giving students multiple ways to access information. Universal Design for Learning examples include giving students multiple ways to demonstrate that they’ve learned the material.
Representation: A key tenet of this approach is providing information in multiple formats. Universal Design for Learning examples include offering students videos, books or interactive demos that cover the material covered in a lecture. Students can then choose the format that best suits their needs.
Engagement: This approach is designed to motivate college students in a variety of ways. Universal Design for Learning examples include assigning projects that relate to a student’s background and interests. This pillar may be put into practice through gamification or discussions.
2.1. Multiple means of action and expression
This topic covers the ‘how’ of learning. It is also referred to as the strategic network. Each learner navigates the classroom in a different way, and they also express themselves uniquely. If someone has an executive function disorder, meaning they have trouble planning, organizing and problem solving, they will express themselves differently than someone who has a language barrier. For example, some postsecondary students are able to use written communication with ease, while others can only communicate through speech.
2.1.1. Executive functions
Executive functions encapsulate a human being’s highest level of skills. This ability allows learners to overcome their impulses to make long-term goals and strategies. Executive functions are limited by the individual’s working memory when executive functioning capacity is devoted to managing “lower level” skills that don’t come as naturally to a person. The individual’s capacity for executive functions can also be lowered by disabilities.
With universal design for learning, instructors can expand a learner’s executive function by scaffolding—that is breaking up lessons into bite-sized pieces—to ensure they do not use up the learner’s working memory. Educators can help students scaffold executive skills so that they can use them more effectively. Setting goals and creating plans to reach these goals can help. Professors can also support students by helping them self-monitor their progress over time, which helps students guide their practice through the semester. It also helps learners better understand what advice to ask their instructors for.3
2.1.2. Expression and communication
Every learner has a different capacity for expression and communication. In addition, various types of media may support different learning styles. For example, a learner who has dyslexia may perform better at expressing their thoughts through verbal conversations versus written essays.
Educators must provide different modalities for communication. This may involve using various types of media or tools for communication. Professors can help students become more fluent in a variety of communication modalities, which will then lend to independent learning.4
2.1.3. Physical action
Interactive software, textbooks and other curricular materials are recognized as Universal Design for Learning examples that can help students understand information. For instance, a postsecondary student may need a different type of workbook if they require assistive technology. These assistive technologies can help students who have disabilities. Some students could benefit from having expanded keyboards or voice-activated switches, for example.5
With this tenet of UDL, it’s important to vary the methods for response and navigation. This involves creating an accessible learning environment that all students can thrive in. The second part of this UDL principle involves opening doors to the tools that will create equal opportunity.
2.2. Multiple means of representation
This topic can be considered the ‘what’ of learning. It’s also referred to as the recognition network. Every learner absorbs the information that is presented to them in a different way. While a sensory or learning disability may cause this difference, cultural differences and different personalities may also affect how the student learns.
2.2.1. Comprehension
The goal of education is to help learners turn information into knowledge that they can apply in their everyday lives. With Universal Design for Learning, educators should get higher ed students involved in transforming and gathering usable knowledge. Gaining usable knowledge is an active process, so students need to be involved in order for this process to work. This means students need to use skills like consumption, categorization and active memorization. They must also learn how to integrate new knowledge with their prior understanding of the subject. The best Universal Design for Learning example here involves relying on organizers such as concept maps to help students visually draw connections between learning units. Metaphors, stories and analogies are also effective in helping students see the importance and relevance of topics.
To achieve these goals, instructors must present information in an accessible way. They should connect the information to previous experiences and offer background information as well. They can highlight relationships, patterns and information so that students can see how the information relates to their goals and other knowledge. Through breaking up lessons into digestible amounts, professors can support students as learners find meaning in their new knowledge and process the information. Finally, educators can help students apply information to new contexts.6
2.2.2. Language, mathematical expressions and symbols
Students interpret linguistic and non-linguistic symbols in different ways. While symbols like division signs provide clarity for some students, others do not understand specific symbols. Likewise, college students attribute different meanings to pictures or words based on their familial backgrounds and culture.
Because of this, educators must provide alternative representations for students. They should clarify different symbols, vocabulary, mathematical notations and syntax. If there is a pattern in different equations, grammar or musical notations, they should be explicit about it. When necessary, instructors should use translations, graphics, movement, activities, simulations, images and videos to make learning active.7 Try pairing a chemistry formula with an illustration or simulation to put this principle into practice.
2.2.3. Perception
Provide the same information through formats that stimulate different senses—ranging from sight, hearing or touch—to make lessons perceptible to learners. This practice will help students absorb information through audio, tactile or visual means. Instructors should also give students formats that can be adjusted such as text that can be increased in size. Other examples of perception include offering alternatives for video and auditory information.8 Such examples are providing videos featuring American Sign Language (ASL) and complementing audio clips with transcripts.
2.3. Multiple means of engagement
Providing multiple means of engagement can be thought of as the ‘why’ of learning. It’s also referred to as the affective network. Every college student is motivated in a different way academically. Likewise, students have different learning styles. While some love spontaneity, others will only feel comfortable when there is a daily routine followed in the classroom. Additionally, some prefer to work alone or in groups.
2.3.1. Self-regulation
Ideally, the learning environment should support and encourage the student’s motivation. When students are in higher education, they should be encouraged to learn how to regulate their own emotions and become self-motivated. By regulating their emotions, students can better cope with changes in their environment. A Universal Design for Learning example includes asking students how they’re feeling today on a scale of 1-5 to understand their needs and any accommodations.
Postsecondary students frequently learn how to self-regulate by observing their professors. Virtual classrooms can encourage this skill by modeling motivation and self-regulation. Then, educators can prompt students to use these abilities in real life. Instructors should discuss their expectations and help students set personal goals. They can support students as they self-assess their abilities and become more aware of their mistakes. Over time, students can learn how to develop healthy emotional responses to a variety of events.9
2.3.2. Sustaining effort and persistence
In order for a student to learn, they must make an effort to pay attention in class. If a student is motivated correctly, they can focus and make a sustained effort in the classroom. Each learner self-regulates in a different way, and these disparities are often due to their motivation levels. Other factors like contextual interference—a disruption in the motor learning phenomenon that affects how people learn and practice new skills—and self-regulation skills can also impact the student’s concentration. This is caused in part by the learner not seeing the value of certain goals and objectives.
Educators can support students by providing different resources to help them learn. Students are more likely to rise to the expectations set to them, so teachers should provide the tools that students need to succeed and help them achieve a certain level of understanding. When introducing goals, instructors should also discuss why each goal matters—put into practice through detailed criteria—so that students feel motivated to achieve each objective.10
2.3.3. Recruiting interest
If the information does not engage the student’s interest, it is essentially inaccessible. Students must be able to absorb and process information in their minds. Each student is interested in different topics of discussion, so teachers must figure out how to gauge their interest in different ways.
Instructors can attract the student’s interest by providing multiple choices whenever it is possible to do so. By allowing for individual choices and autonomy, educators can empower students to take control of their learning. Teachers should also connect the subject matter to experiences outside of the classroom. If university students believe the subject matter has value, they are more likely to be interested in learning it. Finally, teachers should attempt to eliminate distractions so that students have a safe space to learn new information.11
3. Why should you use universal design in your online course?
When using the UDL framework in an online course, you create equal opportunity for students of different backgrounds and abilities to learn the course material. If you are only teaching students in one specific way, many of your students may not be learning the information properly. Universal Design for Learning can help you make your course accessible to all types of learners.
When using the UDL framework in an online course, you create equal opportunity for students of different backgrounds and abilities to learn the course material.
4. How can you apply universal design in your online course?
When using the principles of UDL in your online course, consider the course’s engagement, representation, action and expression goals. Here are four ways to put Universal Design for Learning examples to use in your class.
4.1. Syllabus
Your course syllabus shows college students what they will be learning in the upcoming quarter. It should give students multiple ways to engage with the content, such as through class readings, podcasts, webinars and guest lectures. The syllabus can communicate regular routines, assessment formats and expectations. It should also include the ways that students can access the course content. Consider adding headers and subheadings in your syllabus to make it accessible for screen readers. Download Top Hat’s syllabus template now, available in either Google Docs or Word format.
4.2. Course materials
To follow the principles of universal design, you should select an array of course materials. Instead of only using a textbook, your course can incorporate podcasts, discussion boards, essays, videos and physical activities. By changing the way students can absorb information, you can make the course information accessible to every student.
4.3. Assessment
While some students can take a written test without any issues, others have trouble reading written tests or turning them in. In the realm of assessment, Universal Design for Learning examples might include using video conferencing software to measure student understanding. Depending on the coursework, you could also use assessment techniques like recorded videos.
4.4. Teaching
In order to make your course more accessible, vary the way you teach your course. Images, graphs and textbooks are effective in helping students understand information. Some students learn better through audio tracks, so you may want to use a recorded lecture, video or podcast as well. In addition, you can teach students through discussion groups and other techniques as well.
Top Hat’s Student Engagement toolkit is packed with templates and strategies to create accessible assessments, lesson plans and classroom discussions. Access now.
5. Universal Design for Learning examples
The following list includes Universal Design for Learning examples that you can use to create an equitable and accessible learning environment. You can also create other feedback, assignment and learning options that help diverse learners master the course material in your class.
5.1. Assignment options
Students can achieve the course’s learning objectives through a variety of assignment styles. Beyond traditional homework, students can also submit video recordings of a presentation or speech. They can alternatively create comic strips or podcasts. Another Universal Design for Learning example involves flexible assignment due dates. This policy accommodates students who may have obligations outside of class, such as work or family responsibilities, or who may need additional time due to disabilities or health issues.
5.2. Regular feedback
In order for students to improve, they must regularly receive detailed feedback. Formative assessments can help with this goal, where the student’s ongoing learning is monitored. These assessments also provide feedback that educators can use to improve their course delivery.
5.3. Digital and audio text
Textbooks are just one way that university students can learn. Audiobooks and text-to-speech programs can also help. In addition, you can use audio transcripts and videos with closed captions for students who require assistive technologies. Professors can use multiple modes of communication to convey information, including verbal instructions, written handouts, visual aids, and online resources. These specific Universal Design for Learning examples help students with diverse language abilities, learning styles, and sensory preferences to comprehend the material effectively.
6. Questions to ask when designing an accessible course
6.1. Multiple means of engagement
Does the course encourage independent student responsibilities? In order for college students to learn, they have to be motivated and engaged. When students work independently on coursework, they feel responsible for the outcome. They also become more engaged in studying the material. Consider facilitating surveys or a student interest inventory at the start of the semester to gauge interests, hobbies and strengths. You can then use this information to tailor units of study accordingly.
Can students complete at least some course content at their own pace and in any order they wish? Everyone learns at a different pace. When the course moves too quickly, students can fall behind. Over time, this can cause students to lose motivation. Letting students learn at their own pace in a blended or online course acknowledges their unique circumstances and provides them with the time they need to engage with the course material. Consider implementing an assignment ‘grace period’ to allow students to plan their schedules accordingly based on priorities.
Are course learning goals and outcomes clear? When students are confused or lost, it is impossible for them to become engaged in the course material. You can remove this obstacle by clarifying your learning goals. When students know what to expect and what they need to do, they are more likely to achieve the course’s goals.
6.2. Multiple means of representation
Is the course content provided in multiple ways? With Universal Design for Learning, the goal is to help postsecondary students from all backgrounds and abilities. Students may have visual or aural impairments or have a disability. They may also come from a culture where subject matter is taught differently. Ideally, your course content should be provided through multiple techniques so that everyone has an equal opportunity to learn. Universal Design for Learning examples include complementing course readings with interviews or videos.
Do learning opportunities and assignments use students’ prior knowledge? You can help students retain information by connecting it to their prior knowledge. You can show how new information relates to a previous class or personal experience. Sometimes, you can even assign students the task of reflecting on everything they have learned and how the new topics related to their personal experiences.
Does the course have interactive learning activities online? If you are teaching online, ensure you have multiple ways to get students involved. Interactive activities help students feel like active participants in the learning process. Because students are more likely to learn when they are actively involved, this technique is extremely important for online learners. It ensures that students are just as motivated in their studies, even from a distance. Complement lectures or readings with simulations or other opportunities for students to apply their knowledge.
6.3. Multiple means of action and expression
Have you provided students with note taking support? There are many different disabilities that can make it difficult for students to take notes. To help your students, offer them multiple ways to take notes in class. They can use videos, audio recordings or written techniques to recall information. Some students may also benefit from making graphs or drawings of the new course material.
Does the course include a variety of assessment methods? While some students can take written tests, this is not the best way to assess every student. Universal Design for Learning examples that support a flexible assessment strategy include video interviews, recordings, posters and other techniques. If you use written tests, you may need to offer support such as audio recordings of the questions and answers. Balance summative assessments with low stake formative assessments that enable students to receive more regular and timely feedback.
Are college students encouraged to communicate with faculty and classmates in the course? Open communication between faculty and students—and especially on the student-student level—helps many learn and absorb new information. Ideally, instructors should offer multiple ways for students to communicate with their classmates and teachers. Universal Design for Learning examples could include using online forums, video conferences, interviews or essay feedback to host conversations with students.
Our Teaching with Top Hat Toolkit offers videos and helpful resources to design an accessible learning community in our platform. Browse the toolkit today.
7. The history of Universal Design for Learning
Originally, this technique started out as an architectural concept. In architecture, universal design refers to creating designs that appeal to everyone. These designs must also comply with the Americans with Disabilities Act (ADA). Because of the ADA, many schools began using inclusive facilities and providing equal access to their courses. Originally, the idea of universal design in architecture was created by architect Ron Mace at North Carolina State University.
In 1984, the Center for Applied Special Technology (CAST) was formed. CAST applied the original universal design guidelines to the educational space as a means of facilitating reform. Today, the CAST website houses plenty of instructional design tips and professional development strategies to make the postsecondary education experience more accessible.12
8. Bring UDL to life with Top Hat
Top Hat’s feature suite ensures your students have access to an equitable learning environment. Postsecondary students are able to learn from anywhere—the platform’s offline mode still lets students complete their homework assignments, even without having an Internet connection. Similarly, students can use multiple devices to engage with content stored in Top Hat. Top Hat is compatible with assistive technologies such as screen readers, plus keyboard navigation makes it simple to navigate through content.
Outside of class time, alternative text on images in textbook readings helps students using screen readers understand the visuals alongside the text. Instructors can personalize the learning experience for individual students by customizing assignment due dates and grade weights. This ensures students have multiple ways to express their knowledge—and at a time that suits them.
Top Hat’s accessibility features create a level playing field in your course. Learn more about what Top Hat can do for your students here.
9. References
Morin, A. (n.d.). What is Universal Design for Learning (UDL)? Understood. https://www.understood.org/en/learning-thinking-differences/treatments-approaches/educational-strategies/universal-design-for-learning-what-it-is-and-how-it-works
CAST. (n.d.). The UDL Guidelines. http://udlguidelines.cast.org/
There has been a lot written about the death of the English degree in higher education. Is it true?
Sort of. But there are other interesting trends in patterns across the country in the past dozen years. I downloaded IPEDS data from 2010 to 2022 (even years) and created the visualization to show those changes and patterns in bachelor’s degrees awarded. There are six views, and some of them are interactive.
The first (using the tabs across the top) shows degrees by the institutions where they’re awarded. You can see the college or university sector, region, urbanicity, and Carnegie classification (rolled up into larger segments for clarity.) You’ll see little change: Most degrees are still awarded by public institutions, doctoral institutions, in larger cities. Hover for details.
Over the years, degrees (in first majors) increased about 29% and the second view allows you to see the changes by area (using 2020 CIP codes that cluster degrees in broad areas). You can see the growth in computer science, health professions, and engineering relative to the gray line: All career and professional focus areas; and you can see the drop in traditional degrees in liberal arts.
The third view is identical, but shows growth in second degrees, which increased about 19% over time.
The fourth view also focuses on second majors: Click a single year or the “All” button to drill down or summarize.
The fifth view is highly interactive and allows you to see just what you want to see in terms of the biggest producers of bachelor’s degrees in aggregate or for a specific academic area. Choose a year, academic area, Carnegie type, region and sector, and the filter to size using the slider filter. The view will update to show you wish institutions produce the most degrees in that area.
And finally, if you want to drill down to a single institution, try the last view. It starts showing Oregon State University and five academic areas, but you can change the institution using the filter at the top, and you can add or remove academic areas based on your interests. I recommend no more than five or six for the purpose of clarity, but you do what you want.
As is always the case, the Penn State data are problematic over time due to various names and IPEDS ID designations over time. My tech skills have not figured out a way to normalize this, and I’m not sure it’s worth the effort to do so anyway. You can look up their data on their site if you’re interested.
And one note: The increase in Military Science degrees is over 2000% (on a very small base) and for the sake of clarity, I took it out of the displays showing change over time).
e-Literate is excited to unveil the AI Learning Design Assistant (ALDA) Design/Build Workshop series, a pioneering initiative that brings together a diverse group of colleges and universities to collaboratively tackle the pressing challenges of learning design. This initiative extends beyond standard prompt engineering techniques, inviting participants to participate in co-designing a functioning AI application that we will build and refine throughout the series. It offers participants a unique opportunity to directly influence the development of solutions that will reshape the landscape of educational technology.
Why ALDA?
Despite decades of evolving learning design methodologies, today’s most commonly used tools remain rooted in outdated practices such as word processing and manual note-taking. Meanwhile, the rapid pace of job skill evolution demands more innovative approaches. The ALDA workshop series directly addresses this gap by facilitating a hands-on environment where institutions can collaboratively design, test, and refine AI-driven solutions across six intensive monthly workshops.
Immediate Benefits
Participants will contribute to and gain firsthand experience with cutting-edge technologies poised to revolutionize educational access and quality. This project offers each institution the tools to expand their course offerings and enhance educational quality, significantly impacting their students’ futures.
Participating Institutions
The cohort includes:
Dallas College
Southern New Hampshire University
University of Central Florida
University of Maryland Global Campus
United Negro College Fund, which is including representatives from four Historically Black Colleges and Universities (HBCUs)
Together, these institutions serve over half a million students annually, positioning the cohort to impact educational access on a monumental scale.
Equity Champion Sponsors
D2L and VitalSource are our proud Equity Champion Sponsors, providing scholarships that facilitate cost-free participation for these mission-driven institutions. Their financial support and subject-matter expertise are crucial in paving the way for a future where technology inclusively serves all students.
Supporting Sponsors
Thanks to the generous contributions of Carnegie Mellon University’s Simon Initiative and Engageli, this workshop series has the resources needed to foster robust collaboration and innovation.
Join Us
We look forward to sharing insights and developments from each workshop as we progress.
“UNCF is excited to announce our partnership with the ALDA series and involve historically Black colleges and universities in efforts to co-design a groundbreaking AI application that will revolutionize educational technology. We believe that by harnessing the potential of AI, and involving HBCUs in the creative process, we can launch a transformative tool for faculty members in the development of curricula that will empower every student, regardless of their background or circumstances, to unlock their full potential, and reshape the landscape of educational technology,” said Dr. Shawna Acker-Ball, vice president, scholarships and programs, UNCF. “We look forward to the possibilities this partnership will bring and the positive impact it will have on the lives of students across the nation.”
MJ Bishop, Vice President for Integrative Learning Design at University of Maryland Global Campus shared a similar sentiment: “UMGC’s Integrative Learning Design (ILD) team is thrilled to be part of ALDA cohort and to have the opportunity to pioneer advancements in the use of GAI in instructional design with such an esteemed group of partner institutions and sponsors. We are excited to co-design and refine innovative AI-driven solutions that will enhance our learning design capabilities and significantly impact the educational experiences of our students.”
“I am absolutely thrilled with the quality, diversity, and commitment of the participating organizations,” said Michael Feldstein, CEO of e-Literate. “Artificial intelligence is clearly one of the defining changes of our time with wide-ranging implications for education. We all need to work together and get our hands dirty if we’re going to figure out how best to harness it for our students.”
e-Literate will provide updates as we learn and offer our participants opportunities to share their experiences with you. Institutions and sponsors interested in joining future cohorts or supporting our mission should contact us at [email protected].
LinkedIn is one of my favorite social media platforms for academics. It’s become more popular in the last year, especially since things have changed at Twitter. So I wanted to chat with you about LinkedIn today.
I’m Jennifer van Alstyne of The Social Academic. Today we’re going to be talking about LinkedIn.
Twitter isn’t dead, but the state of social media has changed
So first, the state of social media has changed. I would say since things changed at Twitter, people have been asking me where should I go next?
The answer is that academics are on every social media platform. If you don’t want to use threads or Mastodon or Blue Sky or any of these kind of new platforms that are popping up where you hear academics are spending time, that’s totally okay. They’re still on Facebook, YouTube, Instagram, like TikTok, all the major platforms. I promise that you are going to find an academic audience wherever you feel like spending time.
That being said, LinkedIn is my number one recommendation for professors and researchers. LinkedIn is not just for business people. Professors are finding that their audiences are already there. People like their colleagues, administrators at their university, people that they might be talking with, offices on campus, they know that their collaborators are probably on LinkedIn. Even if they don’t have a profile that they use often, that’s somewhere, that they have potential to connect. Who else? Your research funders, editors, publishers, members of the media like journalists, scientific community, policymakers, all sorts of people who might care about your research in particular are already on LinkedIn.
LinkedIn used to be a social media platform that was really specifically for professionals, especially when they were on the job market when career searching. But academics find that LinkedIn is effective and a good use of their time even when they’re not job searching, and that’s kind of my specialty.
I mostly work with professors who want to have a stronger social media and online presence, but they’re not actually looking for jobs. They just want to be better connected with people in their community.
So first, LinkedIn is not just for business people.
Second, your LinkedIn profile shows up in Google search results. This means that it does have a strong impact on your online presence, and LinkedIn profiles tend to show up at the top of those search results.
So not only does it impact your online presence, it can be a really good answer for people when they know that there’s potential to find what they’re looking for. Oftentimes, people might be looking for contact information. They might be looking for what your current research is about. They might be looking for a photo of you because they’re going to meet you at a conference next month. So I want to let you know there’s many reasons why people might be looking for you online, and your LinkedIn profile might be the answer that they can be looking for.
You don’t need to pay for LinkedIn (or other social media platforms)
LinkedIn is free, so there are many social media platforms that are free. Some of them, they have an option for premium, including LinkedIn. But to be honest, you do not need to be spending money on social media platforms to have a strong online presence.
I don’t spend any money on social media platforms, and even though I have friends that have upgraded to say the premium version of Twitter, like it hasn’t really paid off for them in a way that makes it super effective.
I’m just saying that I don’t actually recommend paying for social media even if you have the budget for it. So LinkedIn is a great free way to build your online presence.
Finding people on LinkedIn with advanced search
I love that LinkedIn has a powerful search. So number three is that LinkedIn profiles really do show up in Google search results, but they also show up when people are searching for your name or something that is on your profile on LinkedIn. So for instance, let’s see, what can I use as an example?
Let’s say an English professor who has a specific focus in American literature has a profile on LinkedIn because they have professor of American literature in their profile, both in their headline and in their about section, that profile will show up on Google search results potentially, but also within LinkedIn search results.
So if I’m looking for professors of English, especially people who focus on American literature, I can type that into the LinkedIn search bar and find all the people that are in, say, the United States. You can even narrow it by specific region or location. So I could find American literature professors in Oklahoma, for instance.
There are so many opportunities to find more people who care about the things that you do, who care about the research that you’re working on within a specific location using that search. So LinkedIn search is really powerful. It’s helpful for finding people within your research interest. It’s also helpful for people finding people at your university or who are alumni of the same programs that you are.
Professors are joining LinkedIn (and updating long-forgotten profiles)
Other professors are on LinkedIn. Number four is that you can find the people that you want to connect with there.
Now, I’m not saying that everyone you know is on LinkedIn, but the chances are at some point in the last decade or so, they’ve created a profile. They’ve created a LinkedIn profile, whether they use it or not, and they have some kind of presence on LinkedIn. This isn’t true for everyone.
I mean, for so long, LinkedIn was known as a platform that is not really for academics. It was kind of so business-y and there wasn’t a lot of conversation that was outside of job searching, and so it wasn’t a place where academics were spending time.
I have seen so many more academics who want to post and share their thought leadership and research on LinkedIn. But there’s also thousands and thousands and thousands of professors who are on LinkedIn and never post at all.
The people that you want to connect with are probably there, and if they’re not there yet, they probably will be soon because like I said, it is a growing platform for professors and researchers.
Inviting people to connect with you, build your scholarly community
Number five is that the other people that are there are people that you probably care about, whether they’re research funders, publishers, editors, members of the community or policymakers, the people outside of the ivory tower who need to find what you’re working on, who can be impacted by it and put it into practice.
There is potential to reach those people on LinkedIn, and I’m saying that there’s potential to do that even if you never post.
I want you to be more open-minded about what LinkedIn could do for you as a professor. It’s not just about job searching. It’s not just about talking about your research or kind of showcasing who you are.
It’s kind of an invitation. I like that LinkedIn profiles when they’re filled out are, it’s like you being open to having people find you and connect with you and reach out to you if it’s relevant to them, even if you never post at all. Having an awesome LinkedIn profile is great for professors.
How long does it take to make my LinkedIn profile?
One question I wanted to be sure to answer today is how long does it typically take to do a LinkedIn profile? That’s a really good question because it really depends.
If you are starting from scratch and all you have is an hour or two to put into your LinkedIn profile, please do it.
Any small change that you can make to your online presence for your LinkedIn profile makes a difference. I encourage you to spend any amount of time that you have in your schedule on making an improved LinkedIn presence for yourself as a professor. It can really help people better connect with you.
How long does it take me to do a LinkedIn profile? Well, it’s taken, kind of different amounts of time for different people, and when I’m doing a done for you profile, it typically takes upwards of 7 hours.
I would say 7-9 hours total is about how long it takes for me to do a LinkedIn profile.
That, to be honest, I could probably spend another 5-7 hours on it and to find even more ways to improve it because that’s how my brain works. The more I understand a platform, the more I understand the person that I’m writing for, the better I can make the profile.
So there’s always opportunity, I think to make a difference with our LinkedIn profiles, but typically the amount of time that I spend for what I would say is a really great LinkedIn profile that meets all of my professor clients’ needs, it’s about 7-9 hours. And that includes a planning meeting, includes everything done for you and a review meeting to make sure that we can make any tweaks or changes in real time.
So it takes quite a bit of time, and that’s why when professors come to me and they’re like, I want a stronger LinkedIn presence, but I know I don’t have time to fit into my calendar. I know, I mean 7-9 of your time is like that’s not only a full day away from your research and the things that matter to you. It’s like time away from your family and the people who you care about. It’s a big commitment, and so I would never, I would never say you have to make this commitment for yourself.
If you don’t have 7-9 hours in your time to work on your LinkedIn profile, I absolutely understand. Please don’t push yourself to do something that is beyond your capacity, especially if this is your first time really being intentional about your online presence on LinkedIn.
Tips for your professor LinkedIn profile
There are so many ways that we can have a stronger online presence, but really any small change that you make makes a difference. So let me tell you the sections of your LinkedIn profile I recommend that you update first because these are the places where people are really looking to know a little bit more about your story and the things that matter to you.
On your LinkedIn profile, the thing that people most often see is your LinkedIn headline. It is a specific number of characters (220), and it’s that little kind of section that goes right next to your profile photo right under your name that people will see when they come across your profile on LinkedIn.
They’ll also see it if they’re already connected with you and they’re seeing you post. So your LinkedIn headline shows up in a lot of places.
When I see professors on LinkedIn, what I most often see is associate professor at university name. That’s actually not enough information for people to know whether they should even click on your profile to learn more about you. They need a little bit more information about your field of research, the area that you teach or the values that you really care about, the change that you want to see in the world. And so if you can fit a little bit more about you, a little bit more detail, a little bit more invitation, that will make a huge difference for your LinkedIn headline.
Profile photo
You’re going to want to have a profile photo that looks like you. On LinkedIn, there is I would say a tendency to use your traditional business headshot, but I wouldn’t feel pressured that you have to use that. If you have a selfie, if you have a friend of you take a photo or a family member.
Let’s see. I have had clients who go out and do professional photo shoots, so there’s lots of options to get new photos that you can use on social media.
But for your LinkedIn profile photo, I really encourage you to have something that is more focused on your face, and that’s because when people are on mobile screens and they’re scrolling it’s kind of small. It’s small enough that if it was more of a upper body shot, I might not recognize you from the photo because your head is then so small that it’s hard to recognize features. So if you can have a little bit of a closer shot for your LinkedIn profile photo, that makes a difference.
Cover photo
Ooh, your cover photo. So one of the first things that people see if they’re seeing the entry for your LinkedIn profile in search results is if you have a cover photo, which is a background photo on other social media platforms.
It’s like a horizontal photo that goes on the top of your profile and gives you opportunity to share a little bit more about yourself through that image. Now, some people prefer something simple like just a solid rectangle of a specific color. So you could just choose your favorite color and use that as your cover photo.
But if you have photos that you’ve taken, photos that you care about or even searching stock images on something like Unsplash or Pexels, in order to find photos that feel like you or maybe remind people of your research, this is a great opportunity to add a touch of personality to your profile.
Now in my LinkedIn for Profiles Course, people do learn how to create a simple cover photo using the design platform Canva, and there are templates already in Canva that you can use to create something that is a little bit more custom. So I want you to know that that’s an option. It’s actually pretty easy to learn. There’s YouTube videos about it.
I want you to know that you have the capability to do fun, custom visual things for your LinkedIn profile, but don’t feel pressured. Again, like I said, if you just choose your favorite color and set that, it will make a difference.
Your LinkedIn About section
Faculty often overlook the about section of your LinkedIn profile. This is a section where you can provide your bio or a little welcome note that says, “Hi, I’m Jennifer, welcome to my profile,” and a little bit about you. So some people write this in the first person, some people write this in the third person, whichever feels more comfortable to you is what I would go with. But this is the number one place people will go to learn more about you.
I don’t recommend just copy and pasting your academic bio. A more general audience is going to be visiting your LinkedIn profile than say, your faculty profile or your Academia.edu.
I want to make sure that one, any jargon is explained, any words that people don’t understand could use a phrase or a sentence of definition.
I want people to know what you do, but I also want them to know what you value, what you care about, why you do what you do. That’s what’s going to help them be curious to explore the rest of your profile or to reach out and connect with you.
I also want people to know how to get in touch with you. So don’t forget to include, say your email address or an invitation to maybe send you a LinkedIn message if you would like people to be able to take that next step and get in touch. Let them know what the best way or preferred method of doing that is.
Experience section
The next section the academics should definitely fill out is the Experience section. This is where you can add your work experience entries. This is a good opportunity to talk about your teaching, to talk about your research. You can even share links to your program so people can learn more about the specific courses that you’re taking or the kind of environment that you’re in.
There’s lots of opportunity to give people more information in the LinkedIn experience section, but if you don’t have a lot of time just filling out the entry with the title of your role, the location of your employer and the time span of which you’ve worked there is enough. If you don’t have time to add details, if you don’t have time to add media links like PDFs or links to maybe the website of the program, I want you to know you don’t have to fill all of that out. The more information that you can give people that is curiosity provoking, that is memorable will help make a difference.
Education section
The Education section is the next place that I know academics typically fill out. In your education section, you have opportunity to include a little bit more information. Some things that academics consider including is if they had fellowships during that time, if they had publications that they were particularly proud of, or if they have maybe an event that they organized and wanted to share a little bit more about.
People also use the detail section of the entry in order to include information that may still help people feel better connected to you. Maybe it’s a description of the type of research that you were working on then, the lab that you were working in, any collaborators that you worked with. It’s a great place to add activities or awards, and I’ve also seen people include information like ‘I’m a first generation student,’ or ‘I had a full fellowship for being a minority STEM student and this is something that I’m really proud of because it made an impact on my education.’
You don’t have to just list things there. This is your space to tell more of your story. Those are the sections that I think matter most. I know there’s so many sections on LinkedIn. The more you fill out, I would say the better.
Publications
One thing to avoid is in the publications section, even if you have a lot of publications, it ends up being just a really long list on your LinkedIn profile. So go ahead and pick maybe four to six publications. I wouldn’t say more than that is particularly helpful, but there’s a great opportunity to share . . . things that you don’t have room for on your LinkedIn profile or you’re not quite sure where it fits in.
Featured
LinkedIn has a featured section. It’s something that goes at the top of your profile, and you can add media like your CV. You could add links. You can add links actually to posts that you’ve shared on LinkedIn or articles. It gives you lots of opportunity to be creative.
If you have a link on a personal website or you want to upload your CV for that full list of publications, please do that. There are opportunities to share more, but on your LinkedIn profile, it is better to be a bit selective with the project section and the publication section because those end up being really long lists on a one page profile.
A nice thing about LinkedIn profiles is that even though there can be a lot of information, people aren’t necessarily shown all of that information at once. Typically, they’re just shown your first few experience entries, and then the more you add, there’s a little show more button that you can click to be able to view that information.
So you can add more information, even if it feels a little bit overwhelming for you, and just know that on the other end when people are experiencing your profile, they’re having the opportunity to choose to see that information and are not forced to or anything. It’s a choice. It’s an exploration, an invitation for people to keep reading.
One question I’ve gotten is about a newer feature on LinkedIn profiles. It’s called Creator Mode, and this is an opportunity for people to have the things that they create, like social media posts on LinkedIn be highlighted on their profile. It has a couple other features, like it switches your connect button over to a follow button.
I have creator mode turned on my profile because I do share posts on LinkedIn.
Creator mode is not right for you if you’re probably not going to post on LinkedIn very often. It’s also probably not right for you if you want people to be able to connect with you easily.
A reason why I have creator mode on is because I post a lot and because of that, people have opportunity to follow me and be able to see my posts. Not all of those people are quite right connections for me. Maybe they’re in another field, but they really care about what I’m saying. I want them to still be able to see my posts.
But in a sense, I also want to protect my audience. I want to protect my connections from any business-y things or sales-y things. I don’t want someone just connecting with me and then spam messaging all of the people that I know, and so I am quite protective over who I connect with and who I don’t. Sometimes business people connect with me. Sometimes coaches connect with me, and I really want to protect the academics that are part of my network from any kind of unwanted messaging.
If you have time to kind of go through connection requests and check out your followers list to see if there’s anyone who didn’t know how to connect with you, but wanted to and you want to connect with them, then turning Creator Mode on makes sense.
Like, you want to have that attention and focus to be able to do those steps because otherwise people are going to not know how to connect with you, and it just changes the profile setup to feature those posts.
If you’re not posting and you don’t have time to do the admin side of being in creator mode, which involves checking those things that I just mentioned, it just doesn’t make sense for you.
Actually for each of my clients, we’ve really talked about what is your professor life? Are you someone who is going to be posting often? Because if you’re not, that’s okay.
If you have a filled out LinkedIn profile and it’s inviting people to connect with you in the ways that make sense for you, then you don’t need to post. Posting is a plus, but it’s not a requirement by any means on LinkedIn or frankly on any other social media platform.
I used to have courses that taught people how to post across platforms. Then I realized that what people need is the ability to post when they want to, how they want to, and they don’t need to, to post everywhere. You don’t need to be everywhere. You don’t need to be consistent even if you can show up intentionally when you do.
When you are posting, if you’re thinking about the people you want to reach and you’re providing them with the information that they need to connect, yay! And if it’s not all the time, that’s totally fine. So posting on social media is great when you have time to do it. It’s great when you want to do it. It’s great skill when you want to get better at it, it can reach so many people. But having a filled out LinkedIn profile I would say is more of an important thing. It’s a better use of your time first.
If you’re someone who’s new to social media or you don’t really post on social media, I still recommend a professor LinkedIn profile for you. So Creator Mode, not recommended for most professors. If you’re going to be posting, and you have time to do some admin checking to see about connections versus followers, then you’re good. Turn it on, it’ll help you reach more people. But if you’re not going to post, don’t bother.
Sending connection requests on LinkedIn
Next, ooh, sending connection requests. Once you have a filled out profile, connecting with people is the next step, and that can feel scary to some professors. Even reaching out to people and care about can still feel a little bit worrying, like a little bit like a task that would be easy to avoid. That’s totally okay. I get it. I get it.
Connecting with people can feel awkward. One thing that people worry about is like, “Oh my gosh, it’s going to take me so much time to write messages to each of those people, and I don’t know. What if they don’t even read it?” But I would say if you have a filled out LinkedIn profile, there’s not really a need to send personalized notes to people. If they are visiting your profile and they’re like, “I don’t know what I would ever talk about this person with. I have no idea who they are, and I can’t see myself even having a conversation with them because their profile and what I do and what I think about isn’t really aligned.” I mean, that happens and that’s okay.
Your profile can be kind of that invitation. Your profile when you put thought and intention into it can really help people know whether you’re a good person to connect with or not. And if you are reaching out and connecting with them, my guess is that you probably know them. So all they need to see is your filled out profile.
You may get some messages from people who you’ve reached out to connect with that are like, “Oh my gosh, I’m so excited you’re on LinkedIn!” That’s what happened to my recent professor client once he joined after many years of people telling him he should and not having the time to do it. I mean, it’s understandable. He has children, he has a family, he has research, he has priorities.
You’re a professor with priorities too. It’s okay if your LinkedIn profile isn’t one of them.
Get support for your LinkedIn profile
There are different ways to have a stronger social media presence on LinkedIn.
You can do it yourself
You can totally do this yourself, even if you only have an hour today to set up your LinkedIn profile, and all you do are your headline, your profile photo, and your bio, I will be so proud of you.
That will be a huge plus for you. So please know that any amount of time that you spend on your own LinkedIn profile is great.
All you have to do is a planning meeting with me. We’ll talk about your CV. We’ll talk about the people that you want to reach. We’ll talk about your goals, and if you have things to share with me, if you have projects that you care about and you want me to make sure to include that report that made a difference in people’s lives, we’ll gather all those materials first.
Then on your VIP day, it’s all about your LinkedIn profile. We’ll meet in the morning to get me set up so I have access to your profile. I’ll do the whole profile for you. It’s like a process that you really, you can focus on the things you need to that day.
I might email you a question, but for the most part, I’m doing all of the work for you so you can relax and have the weekend to focus on your family, to have that date focus on your teaching and your research and the things that you care about most.
Once the day is over, we typically meet the next morning, the next day to make sure that we can look at your LinkedIn profile together, make any needed changes in real time so that your profile looks and feels like you.
We always actually have some changes to the cover photo. So cover photos are something that people often are like, “oh, I don’t really have any ideas.” But once we get talking about it, we’re like, “oh, how can we connect this to the places that I love or the research that I care about?” Or people actually find sometimes they have photos of them with their students or photos of them on campus. That is something that we can use for that space.
There’s a lot of opportunity for us to be creative together about how to make your LinkedIn profile more personal, more colorful, more you.
After we review your profile, there’s always time for training on how to use LinkedIn because most of the faculty that come to me, they don’t know how to use LinkedIn at all. They don’t necessarily want to post. Some professors tell me straight up, “I’m probably not ever going to post,” and I say, “That’s okay.”
That’s okay because this work on your profile is still going to help people connect with you. It’s still going to help people be able to find you, share your research, and do these things that you care about, these goals that we’ve talked about.
Now, when you don’t post on LinkedIn, that’s great. You don’t have to, but LinkedIn you should know is a place that you can post. You can post sporadically, you can post longer things like articles. You can have live events, you can share videos. You can share photos and PDFs and reports.
There’s lots of opportunities to share things on LinkedIn if that’s something that you are curious about. So we do personalized training at the end of your LinkedIn, VIP day, at the end of that profile review meeting to make sure that you know how to do the things on LinkedIn that’s going to make sense for your life, for your goals, and for the things that you actually want to accomplish with LinkedIn.
If that’s really networking and connecting with the people, the people that you care about most, we can actually start doing that process together. That’s what my last client and I did, and we had so much fun reaching out to some past students and making sure that we were connecting with people in his life that mattered and making sure that we were connecting with people at the university.
There’s lots of opportunity for us to move your LinkedIn presence and your social media profiles together. Really a full transformation, not only on what goes on the profile, but how you use the platform during that LinkedIn VIP day.
Free resources to support your LinkedIn presence as an academic
If you’re someone who’s like, wow, that sounds amazing, but I think that that’s more than I even want to do. Just start. I have free resources to share with you on The Social Academic that help you update different sections of your profile.
I have one that’s specifically for graduate students, so I’m going to share all of these resources with this podcast episode.
I want you to know that any small change that you do for your LinkedIn profile makes a difference. You do not need to work with me in order to have a great LinkedIn profile.
I work with, I would say, mid-career academics, senior career academics, higher education administrators, principal investigators, people who really have a lot on their plate. They know that this is going to make a difference for the people that they support, the communities that they want to reach and the communities that they care about, but they’re just not going to have the time.
So if that’s you, if you’re someone who knows that you’re not going to have the time, but that this is something that you need, I’m here to help you.
My name is Jennifer van Alstyne. Thank you so much for listening to this episode all about LinkedIn profiles and why LinkedIn is amazing for academics.
EdTech seems to go through perpetual cycles of infatuation and disappointment with some new version of a personalized one-on-one tutor available to every learner everywhere. The recent strides in generative AI give me hope that the goal may finally be within reach this time. That said, I see the same sloppiness that marred so many EdTech infatuation moments. The concrete is being poured on educational applications that use a very powerful yet inherently unpredictable technology in education. We will build on a faulty foundation if we get it wrong now.
I’ve seen this happen countless times before, both with individual applications and with entire application categories. For example, one reason we don’t get a lot of good data from publisher courseware and homework platforms is that many of them were simply not designed with learning analytics in mind. As hard as that is to believe, the last question we seem to ask when building a new EdTech application is “How will we know if it works?” Having failed to consider that question when building the early versions of their applications, publishers have had a difficult time solving for it later.
In this post, I propose a programmatic, sector-wide approach to the challenge of building a solid foundation for AI tutors, balancing needs for speed, scalability, and safety.
The temptation
Before we get to the details, it’s worth considering why the idea of an AI tutor can be so alluring. I have always believed that education is primal. It’s hard-wired into humans. Not just learning but teaching. Our species should have been called homo docens. In a recent keynote on AI and durable skills, I argued that our tendency to teach and learn from each other through communications and transportation technologies formed the engine of human civilization’s advancement. That’s why so many of us have a memory of a great teacher who had a huge impact on our lives. It’s why the best longitudinal study we have, conducted by Gallup and Purdue University, provides empirical evidence that having one college professor who made us excited about learning can improve our lives across a wide range of outcomes, from economic prosperity to physical and mental health to our social lives. And it’s probably why the Khans’ video gives me chills:
Check your own emotions right now. Did you have a visceral reaction to the video? I did.
Unfortunately, one small demonstration does not prove we have reached the goal. The Khanmingo AI tutor pilot has uncovered a number of problems, including factual errors like incorrect math and flawed tutoring. (Kudos to Khan Academy for being open about their state of progress by the way.)
We have not yet achieved that magical robot tutor. How do we get there? And how will we know that we’ve arrived?
Start with data scientists, but don’t stop there
As I read some of the early literature, I see an all-too-familiar pattern: technologists build the platforms, data scientists decide which data are important to capture, and they consult learning designers and researchers. However, all too often, the research design clearly originates from a technologist’s perspective, showing relatively little knowledge of detailed learning science methods or findings. A good example of this mindset’s strengths and weaknesses is Google’s recent paper, “Towards Responsible Development of Generative AI for Education: An Evaluation-Driven Approach“. It reads like a paper largely concieved by technologists who work on improving generative AI and sharpened up by educational research specialists they consulted with after they already had the research project largely defined.
The paper proposes evaluation rubrics for five dimensions of generative AI tutors:
Clarity and Accuracy of Responses: This dimension evaluates how well the AI tutor delivers clear, correct, and understandable responses. The focus is on ensuring that the information provided by the AI is accurate and easy for students to comprehend. High clarity and accuracy are critical for effective learning and avoiding the spread of misinformation.
Contextual Relevance and Adaptivity: This dimension assesses the AI’s ability to provide responses that are contextually appropriate and adapt to the specific needs of each student. It includes the AI’s capability to tailor its guidance based on the student’s current understanding and the specific learning context. Adaptive learning helps in personalizing the educational experience, making it more relevant and engaging for each learner.
Engagement and Motivation: This dimension measures how effectively the AI tutor can engage and motivate students. It looks at the AI’s ability to maintain students’ interest and encourage their participation in the learning process. Engaging and motivating students is essential for sustained learning and for fostering a positive educational environment.
Error Handling and Feedback Quality: This dimension evaluates how well the AI handles errors and provides feedback. It examines the AI’s ability to recognize when a student makes a mistake and to offer constructive feedback that helps the student understand and learn from their errors. High-quality error handling and feedback are crucial for effective learning, as they guide students towards the correct understanding and improvement.
Ethical Considerations and Bias Mitigation: This dimension focuses on the ethical implications of using AI in education and the measures taken to mitigate bias. It includes evaluating how the AI handles sensitive topics, ensures fairness, and respects student privacy. Addressing ethical considerations and mitigating bias are vital to ensure that the AI supports equitable learning opportunities for all students.
Of these, the paper provides clear rubrics for the first four and is a little less concrete on the fifth. Notice, though, that most of these are similar dimensions that generative AI companies use to evaluate their products generically. That’s not bad. On the contrary, establishing standardized, education-specific rubrics with high inter-rater reliability across these five dimensions is the first component of the programmatic, sector-wide approach to AI tutors that we need. Notice these are all qualitative assessments. That’s not bad but, for example, we do have quantitative data available on error handling in the form of feedback and hints (which I’ll delve into momentarily).
That said, the paper lacks many critical research components, particularly regarding the LearnLM-Tutor software the researchers were testing. Let’s start with the authors not providing outcomes data anywhere in the 50-page paper. Did LearnLM-Tutor improve student outcomes? Make them worse? Have no effect? Work better in some contexts than others? We don’t know.
We also don’t know how LearnLM-Tutor incorporates learning science. For example, on the question of cognitive load, the authors write,
We designed LearnLM Tutor to manage cognitive load by breaking down complex tasks into smaller, manageable components and providing scaffolded support through hints and feedback. The goal is to maintain an optimal balance between intrinsic, extraneous, and germane cognitive load.
Towards Responsible Development ofGenerative AI for Education: An Evaluation-Driven Approach
How, specifically, did they do this? What measures did they take? What relevant behaviors were they able to elicit from their LLM-based tutor? How are those behaviors grounded in specific research findings about cognitive load? How closely do they reproduce the principals that produced the research findings they’re drawing from? And did it work?
We don’t know.
The authors are also vague about Intelligent Tutoring Systems (ITS) research. They write,
Systematic reviews and meta-analyses have shown that intelligent tutoring systems (ITS) can significantly improve student learning outcomes. For example, Kulik and Fletcher’s meta-analytic review demonstrates that ITS can lead to substantial improvements in learning compared to traditional instructional methods.
Towards Responsible Development ofGenerative AI for Education: An Evaluation-Driven Approach
That body of research was conducted over a relatively small number of ITS implementations because a relatively small number of these systems exist and have published research behind them. Further, the research often cites specific characteristics of these tutoring systems that lead to positive outcomes, with supporting data. Which of these characteristics does LearnLM Tutor support? Why do we have reason to believe that Google’s system will achieve the same results?
We don’t know.
I’m being a little unfair to the authors by critiquing the paper for what it isn’t about. Its qualitative, AI-aligned assessments are real contributions. They are necessary for a programmatic, sector-wide approach to AI tutor development. They simply are not sufficient.
ITS data sets for fine-tuning
ITS research is a good place to start if we’re looking to anchor our AI tutor improvement and testing program in solid research with data sets and experimental protocols that we can re-use and adapt. The first step is to explore how we can utilize the existing body of work to improve AI tutors today. The end goal is to develop standards for integrating the ongoing ITS research (and other data-backed research streams) into continuous improvement of AI tutors.
One key short-term opportunity is hints and feedback. If, for the moment, we stick with the notion of a “tutor” as software engaging in adaptive, turn-based coaching of students on solving homework problems, then hints and feedback are core to the tutor’s function. ITS research has produced high-quality, publicly available data sets with good findings on these elements. The sector should construct, test, and refine an LLM fine-tuning data set on hints and feedback. This work must include developing standards for data preprocessing, quality assurance, and ethical use. These are non-trivial but achievable goals.
The hints and feedback work could form a beachhead. It would help us identify gaps in existing research, challenges in using ITS data this way, and the effectiveness of fine-tuning. For example, I’d be interested in seeing whether the experimental designs used in hints and feedback ITS research papers could be replicated with an LLM that has been fine-tuned using the research data. In the process, we want to adopt and standardize protocols for preserving student privacy, protecting author rights, and other concerns that are generally taken into account in high-quality IRB-approved studies. These practices should be baked into the technology itself when possible and supported by evaluation rubrics when it is not.
While this foundational work is being undertaken, the ITS research community could review its other findings and data sets to see which additional research data sets could be harnessed to improve LLM tutors and develop a research agenda that strengthens the bridge being built between that research and LLM tutoring.
The larger limitations of this approach will likely spring the uneven and relatively sparse coverage of course subjects, designs, and student populations. We can learn a lot about developing a strategy for uses these sorts of data from ITS research. But to achieve the breadth and depth of data required, we’ll need to augment this body of work with another approach that can scale quickly.
Expanding data sets through interoperability
Hints and feedback are great examples of a massive missed opportunity cost. Virtually all LMSs, courseware, and homework platforms support feedback. Many also support hints. Combined, these systems represent a massive opportunity to gather data about usage and effectiveness of hints and feedback across a wide range of subjects and contexts. We already know how the relevant data need to be represented for research purposes because we have examples from ITS implementations. Note that these data include both design elements—like the assessment question, the hints, the feedback, and annotations about the pedagogical intent—and student performance when they use the hints and feedback. So if, for example, we were looking at 1EdTech standards, we would need to expand both Common Cartridge and Caliper standards to incorporate these elements.
This approach offers several benefits. First, we would gain access to massive cross-platform data sets that could be used to fine-tune AI models. Second, these standards would enable scaled platforms like LMSs to support proven metheds for testing the quality of hints and feedback elements. Doing so would provide benefit to students using today’s platforms while enabling improvement of the training data sets for AI tutors. The data would be extremely messy, especially at first. But the interoperability would enable a virtuous cycle of continuous improvement.
The influence of interoperability standards on shaping EdTech is often underestimated and misunderstood. !EdTech was first created when publishers realized they needed a way to get their content into new teaching systems that were then called Instructional Management Systems (IMS). Common Cartridge was the first standard created by the organization now known as 1EdTech. Later, Common Cartridge export made migration from one LMS to another much more feasible, thus aiding in breaking the product category out of what was then a virtual monopoly. And I would guess that perhaps 30% or more of the start-ups at the annual ASU+GSV conference would not exist if they could not integrate with the LMS via the Learning Tool Interoperability (LTI) standard. Interoperability is a vector for accelerating change. Creating interoperabiltiy around hints and feedback—including both the importing of them into learning systems and passing student performance impact data—could accelerate the adoption of effective interactive tutoring responses, whether they are delivered by AI or more traditional means.
Again, hints and feedback are the beachhead, not the end game. Ultimately, we want to capture high-quality training data across a broad range of contexts on the full spectrum of pedagogical approaches.
Capturing learning design
If we widen the view beyond the narrow goal of good turn-taking tutorial responses, we really want our AI to understand the full scope of pedagogical intent and which pedagogical moves have the desired effect (to the degree the latter is measurable). Another simple example of a construct we often want to capture in relation to the full design is the learning objective. ChatGPT has a reasonably good native understanding of learning objectives, how to craft them, and how they relate to gross elements of a learning design like assessments. It could improve significantly if it were trained on annotated data. Further, developing annotations for a broad spectrum of course design elements could improve its tutoring output substantially. For example, well-designed incorrect answers to questions (or “distractors”) often test for misconceptions regarding a learning objective. If distractors in a training set were specifically tagged as such, the AI could better learn to identify and probe for misconceptions. This is a subtle and difficult skill even for human experts but it is also a critical capability for a tutor (whether human or otherwise).
This is one of several reasons why I believe focusing effort on developing AI learning design assistants supporting current-generation learning platforms is advantageous. We can capture a rich array of learning design moves at design time. Some of these we already know how to capture through decades of ITS design. Others are almost completely dark. We have very little data on design intent and even less on the impact of specific design elements on achieving the intended learning goals. I’m in the very early stages of exploring this problem now. Despite having decades of experience in the field, I am astonished at the variability in learning design approaches, much of which is motivated and little of which is tested (or even known within individual institutions).
On the other side, at-scale platforms like LMSs have implemented many features in common that are not captured in today’s interoperability standards. For example, every LMS I know of implements learning objectives and has some means of linking them to activities. Implementation details may vary. But we are nowhere close to capturing even the least-common-denominator functionality. Importantly, many of these functions are not widely used because of the labor involved. While LMSs can link learning objectives to learning activities, many course builders don’t do it. If an AI could help capture these learning design relationships, and if it could export content to a learning platform in a standard format that preserves those elements, we would have the foundations for more useful learning analytics, including learning design efficacy analytics. Those analytics, in turn, could drive improvement of the course designs, creating a virtuous cycle. These data could then be exported for model training (with proper privacy controls and author permissions, of course). Meanwhile, less common features such as flagging a distractor as testing for a misconception could be included as optional elements, creating positive pressure to improve both the quality of the learning experiences delivered in current-generation systems and the quality of the data sets for training AI.
Working at design time also puts a human in the loop. Let’s say our workflow follows these steps:
The AI is prompted to conduct turn-taking design interviews of human experts, following a protocol intended to capture all the important design elements.
The AI generates a draft of the learning design. Behind the scenes, the design elements are both shaped by and associated with the metadata schemas from the interoperability standards.
The human experts edit the design. These edits are captured, along with annotations regarding the reasons for the edits. (Think Word or Google Docs with comments.) This becomes one data set that can be used to further fine-tune the model, either generally or for specific populations and contexts.
The designs are exported using the interoperability standards into production learning platforms. The complementary learning efficacy analytics standards provide telemetry on the student behavior and performance within a given design. This becomes another data set that could potentially be used for improving the model.
The human learning designers improve the course designs based on the standards-enabled telemetry. They test the revised course designs for efficacy. This becomes yet another potential data set. Given this final set in the chain, we can look at designer input into the model, the model’s output, the changes human designers made, and improved iterations of the original design—all either aggregated across populations and contexts or focused on a specific population and context.
This can be accomplished using the learning platforms that exist today, at scale. Humans would always supervise and revise the content before it reaches the students, and humans would decide which data they would share under what conditions for the purposes of model tuning. The use of the data and the pace of movement toward student-facing AI become policy-driven decisions rather than technology-driven. At each of the steps above, humans make decisions. The process allows for control and visibility regarding the plethora of ethical challenges that face integrating AI into education. Among other things, this workflow creates a policy laboratory.
This approach doesn’t rule out simultaneously testing and using student-facing AI immediately. Again, that becomes a question of policy.
Conclusion
My intention here has been to outline a suite of “shovel-ready” initiatives that could be implemented realitvely quickly at scale. It is not comprehensive; nor does it attempt to even touch the rich range of critical research projects that are more investigational. On the contrary, the approach I outline here should open up a lot of new territory for both research and implementation while ensuring the concrete already being poured results in a safe, reliable, science- and policy-driven foundation.
We can’t just sit by and let AI happen to us and our students. Nor can we let technologists and corporations become the primary drivers of the direction we take. While I’ve seen many policy white papers and AI ethics rubrics being produced, our approach to understanding the potential and mitigating the risks of EdTech AI in general and EdTech tutors in particular is moving at a snail’s pace relative to product development and implementation. We have to implement a broad, coordinated response.
If you are a mature student, returner to learning or someone who has experienced a disrupted education, you may be anxious about the support available for anyone not familiar with higher education and its challenges. Starting your higher education journey is one of the most exciting times of your life, but we realise you might have some concerns as well. Whatever they might be, you don’t need to worry as the University of Liverpool offers lots of support.
OurStudent Services team, who offer a huge range of services, including mental health support like counselling, a Mental Health Advisory Service and wellbeing support including self-help guides, workshops and events. They also provide financial advice, including guidance on managing the rising cost of living and support for disabled students through initiatives like Disability Coaches, a peer support service of trained students with lived experience of disability and accessing disability support. Disability Coaches can help with initial enquiries, support plans, obtaining medical evidence and Disabled Students’ Allowance (DSA).
The Liverpool Guild of Students offer free and confidential advice to all students about the options available to you, covering academic, housing, wellbeing issues and more. Through the Guild you can access a huge range of Societies, providing a brilliant opportunity to make new friends through shared interests. They also provide schemes like Give It A Go and lots of volunteering programmes, giving you the chance to enhance your student experience.
From September 2024, Go Higher students will be able to access Liverpool Plus, a brand new post-entry support programme. Including an Enhanced Welcome package, 1-2-1 support with your transition into first year, bespoke events with University services like Global Opportunities and Libraries, and priority access to schemes like the Liverpool Advocate programme.
With Liverpool Plus, we’ll provide the support you need to make the most out of your time at University
