AI in Education – Chat-GPT and a Serious Concern

While there has been considerable attention recently about Artificial Intelligence, AI, development in the field is not new. In 1956 Dartmouth college coined the term during a summer research project organized by Dr. John McCarthy.


Since then, “artificial intelligence” has been used in one way or another in education for years. Search engines, personal assistants on phones, assistive technology to increase accessibility, and other technology all use some form of applied artificial intelligence. However, what most people are concerned about is the advances OpenAI technology found in GPT-4 and ChatGPT. To address this, let's briefly look at how ChatGPT works and a few of the issues they pose through their architecture.

CHAT-GPT: How it Works

  Unlike Deep Blue, that uses “brute force” to check all possible outcomes to determine an optimal answer, ChatGPT uses an artificial neural network that trains on data sets from billions of webpages from the internet, with trillions of words, and selects a word that statistically will follow its predecessor. Artificial Neural Networks have been around for a long time, and they are excellent at pattern matching. In the case of ChatGPT, the program weights all the words that have been connected to the input and selects the word that has the highest probability the weights have assigned to it. The program then looks for the next word the same way.

GPT-3 (the program the runs ChatGPT) used over 3000 HGX A100 servers with over 28,000 GPS to train on over 570 GB of text data to train and assign weights to words. With months of training, it created over 170 billion connections between all the words and added weights representing the importance of each connected relative to the word. This level of computing was very costly, at about $500,000/day and using more electricity than 150,000 people use in a month (23M KWh). The trained Artificial Neural Network then had researchers ‘tweak’ the weights to help rule out odd responses and was trained until the responses were consistent to what the programmers want.

This results in a program that looks for the next -token prediction from a list of rated words as the word that will likely appear next. There is no semantic grounding, set knowledge base of information based on confirmed scientific data, or any criterion of truth other than that of the statistically probability of another work following your prompt given its large training set. For example, when you use CHATGPT you:

1. Give it a prompt
2. ChatGPT looks at the last word of the prompt and assigns a number to encode it.
3. It then multiplies that number by the connections of everything it learned how words are associated with each other (embedded) which creates a 12,000-dimensional matrix
4. Attention transformers that identify which words in the prompt should have more attention that others (such as nouns over adverbs)
5. “normalize” output to make it seem more like a matrix again
6. Feed results forward to another layer of attention transformers (repeat 95 times)
7. Produce one word
8. ChatGPT repeats steps 2-7 for the rest of the words in the prompt.

At no point does ChatGPT know what the question is.

There is no knowledge-base being consulted.


It just works assign weights to nodes in a matrix representing the probability of a word that is most likely to follow its predecessor and selecting the one with the highest value. These weights are assigned through a training algorithm, that includes a ‘randomness” to ensure that the text appears “fresh”, working with a training set, as well as researchers “tweak” the weights to help create desired results.

It seems clear that one of the desired results is to create a program that appears intelligent regardless of knowledge or being accurate. This might not be too surprising when we remember that ChatGPT is a product of a private company where perception of a successful product is key for profit and increasing stock prices. Moreover, there is a lot of pressure to sell these systems. The current costs of these AI systems are creating a money pit for investors while the product seems to be a solution looking for a problem

What Could Go Wrong?

Misinformation and the Lack of Truth

ChatGPT may be designed to present its information in a convincing way. Since the first chatbot, ELIZA, people tend to anthropomorphize and place trust in these software applications. This can be dangerous with a program that can produce misleading statements. While there is a disclaimer to ChatGPT, OPEN AI benefited from the hype while not warning of issues with their product.

ChatGPT is capable of generating a considerable amount of nonsense, such as:

“Some experts believe that the act of eating a sock helps the brain to come out of its altered state as a result of meditation.”

While this is noticeably absurd, other statements may not be obvious. ChatGPT has already been documented to fabricate information and to adamantly defend these fabrications. Often these cases are referred to as “hallucinations”, as the chatbot produces responses as though they are correct. Apparently ChatGPT has been so convincing, that already a lawyer has been caught citing cases hallucinated by the program. This was a career ending mistake.

OPENAI recommends that users check the output of ChatGPT. However, not producing accurate information seems to be a serious flaw of the tool. Instead, ChatGPT is producing vast amounts of text with a variable level of “truthiness”. Another term for its output would be fiction or misinformation. Others argue that its output conforms with the technical term coined by Harry Frankford – the term ‘bullshit ’. It is named this in reference to the game where actors deliberately try to convince others of a statement with no regard to its truth.

In small doses misinformation on the web is not a problem, however AI has already produced more text than humans have since the Guttenberg printing press. Each day ChatGPT produces approximately 4.5 billion words a day. This flood of information will make it harder to find accurate and truthful information on the web. This is harmful on many levels, including contributing to undermining democracies. If colleges are going to promote the whole scale adoption of this technology, we do have to consider the increase in misinformation and resources that this will produce. It may be wise to have a more tempered approach.

Whenever a new language model like ChatGPT comes out, it gets a lot of hype. However, how should we proceed? We are not faced with the dilemma of promoting the misinformation spread by encouraging it or banning all AI reminiscent of human history in Herbert’s book Dune. Another option includes employing a measured approach where we carefully employ AI to illustrate its flaws and prepare students for the future. This may include how to combat a vast amount of misinformation and should certainly include highlighting the importance of information literacy and research librarians, who are regularly under-utilized by students.

ChatGPT may be designed to present its information in a convincing way. Since the first chatbot, ELIZA could encourage people to anthropomorphize and place trust in the software application. This can be dangerous when a program that can produce misleading statements. While there is a disclaimer to ChatGPT, OPEN AI benefited from the hype while not warning of issues with their product.

ChatGPT is capable of generating a considerable amount of nonsense, such as:

“Some experts believe that the act of eating a sock helps the brain to come out of its altered state as a result of meditation.”

While this is noticeably absurd, other statements may not be obvious. ChatGPT has already been documented to fabricate information and to adamantly defend these fabrications. Often these cases are referred to as “hallucinations”, as the chatbot produces responses as though they are correct. Apparently ChatGPT has been so convincing, that already a lawyer has been caught citing cases hallucinated by the program. This was a career ending mistake.

OPENAI recommends that users check the output of ChatGPT.  However, not producing accurate information seems to be a serious flaw of the tool. Instead, ChatGPT is producing vast amounts of text with a variable level of “truthiness”.  Another term for its output would be fiction or misinformation. Others argue that its output conforms with the technical term coined by Harry Frankford – it is  ‘bullshit’.  

In small doses misinformation on the web is not a problem, however AI has already produced more text than humans have since the Guttenberg printing press. Each day ChatGPT produces approximately 4.5 billion words a day. This flood of information will make it harder to find accurate and truthful information on the web. This is harmful on many levels, including contributing to undermining democracies. If colleges are going to promote the whole scale adoption of this technology, we do have to consider the increase in misinformation and resources that this will produce. It may be wise to have a more tempered approach.

Whenever a new language model like ChatGPT comes out, it gets a lot of hype. However, how should we proceed? We are not faced with the dilemma of promoting the misinformation spread by encouraging it or banning all AI reminiscent of human history in Herbert’ book Dune. Another option includes employing a measured approach where we carefully employ AI to illustrate its flaws and prepare students for the future. This may include how to combat a vast amount of misinformation and should certainly include highlighting the importance of information literacy and research librarians, who are regularly under-utilized by students.

Reference  

Berry, D (2018) "Weizenbaum, ELIZA and the End of Human Reason". In Baranovska, Marianna; Höltgen, Stefan (eds.). Hello, I'm Eliza: Fünfzig Jahre Gespräche mit Computern [Hello, I'm Eliza: Fifty Years of Conversations with Computers] (in German) (1st ed.). Berlin: Projekt Verlag. pp. 53–70.

Bobrowsky, M (2023) The Metaverse is Quickly Turning into the Meh-taverse , The Wall Street Journal. Mar 29.

Colom R, Karama S, Jung RE, Haier RJ. (2010) Human intelligence and brain networks. Dialogues Clin Neurosci. 12(4):489-501

DeGeurin, M (2023) 'Thirsty' AI: Training ChatGPT Required Enough Water to Fill a Nuclear Reactor's Cooling Tower, Study Finds. Gizmodo. May 10.

Dotan, T (2023) Big Tech Struggles to Turn AI Hype Into Profits. The Wall Street Journal. Oct 9.

Ellingrud, K., Sanghvi, S. Dandona, G, Madgavkar, A, Chu, M, White, O and P. Hasebe (2023) Generative AI and the Future of Work in America. McKinsey Global Institute. July 25.

Estreich, G (2019) Fables and Futures: Biotechnology, Disability, and the Stories We Tell Ourselves, Cambridge, MA: MIT Press.

Frankfurt, H. G. (1988) “On Bullshit.” The Importance of What We Care About: Philosophical Essays, pp. 117–133. Cambridge: Cambridge University Press (originally published in the Raritan Quarterly Review, 6(2): 81–100, 1986; reprinted as a book in 2005 by Princeton University Press).

Gebru, T, Morgenstern J, Vecchione, B, Wortman Vaughan, J, Wallack, H, Daume, H, and K. Crawford (2022) Excerpt from Datasheets for Datasets. In Ethics of Data and Analytics: Concepts and Cases. Martin, K. New York: Auerbach Publications

Hicks, M (2023) No, ChatGPT Can’t be Your New Research Assistant. Chronicle of Higher Education. Ag 23.

Hetzner, C (2023) ChatGPT moves to cash in on its fame as OpenAI launches plan to charge monthly fee for premium subscribers. Fortune. January 23.


Hopfield, J. J. (1982). "Neural networks and physical systems with emergent collective computational abilities". Proceedings of the National Academy of Sciences. 79 (8): 2554–2558

Knight, W (2022) ChatGPT’s Most Charming Trick Is Also Its Biggest Flaw. Wired. Dec 7.

Korhonen, P (2023) AI is a solution in search of a problem. UXDesign. Feb 2

Ludvigsen (2023) ChatGPT’s Electricity Consumption, Part II Medium. Mar 5

Magesh, V (2024) AI on Trial: Legal Models Hallucinate in 1 out of 6 (or More) Benchmarking Queries, HAI. Stanford University. May 23.

McDermott, D (1976) Artificial Intelligence Meets Natural Stupidity. SIGART Newsletter, 57:4-9

McMurtrie, B (2023) Teaching: Want your students to be skeptical of ChatGPT? Chronical of Higher Education. Sep 21

Merken, S (2023) Lawyer who cited cases concocted by AI asks judge to spare sanctions. Reuters. June 8.

Mollick, E (2023) Centaurs and Cyborgs on the Jagged Frontier. One Useful Thing, Sep 16.

Morgan, S. (2018) Fake news, disinformation, manipulation and online tactics to undermine democracy, Journal of Cyber Policy, 3:1, 39-43.

Newell, A. (1963). A Guide to the General Problem-Solver Program GPS-2-2. RAND Corporation, Santa Monica, California. Technical Report No. RM-3337-PR.

Newell, A.; Shaw, J.C.; Simon, H.A. (1959). Report on a general problem-solving program. Proceedings of the International Conference on Information Processing. pp. 256–264.

Norvig, Peter (1992), ELIZA: Paradigms of Artificial Intelligence Programming, San Francisco: Morgan Kaufmann Publishers

Olson, P. (2023) There’s No Such Thing as Artificial Intelligence. The Washington Post. Mar 26.

Ouyang, L, et al (2022) Training language models to follow instructions with human feedback. OpenAI

Pawar, S (2023) ChatGPT Costs a Whopping $700,000/Day to Operate, Says Research Analytics Drift. April 25.

Roose, K. (2023) Don’t ban ChatGPT in Schools. Teach with it. The New York Times. Jan. 12

Ropek, L (2023) So Far, AI Is a Money Pit That Isn't Paying Off Gizmodo. Oct 10.

Vincent, J (2021) OpenAI’s text-generating system GPT-3 is now spewing out 4.5 billion words a day. The Verge. Mar 29.

Valdez, E (2023) How much is invested in Artificial Intelligence Exoinsight. Mar 23.

Wolfram, S. (2023) What is ChatGPT Doing … and Why Does It Work? Stephen Wolfram Writings.

Cheating the Benefits of Cheating with AI

With the popularity and extraordinary, and dubious, claims of the ability of AI (Narayanan & Kapoor, 2024), a heightened concern about students cheating has occurred. Naturally, proctoring and anti-plagiarism companies benefit from this fear and the sales of their ‘solutions’ increase.  The problem with this policing solution is that beyond a point, teachers get into an ‘arms race’ between AI and AI-detection software where the real loser is the institution footing the bill.  One might think that the companies are happy to ‘stir the pot’ to increase anxiety for the benefit of their shareholders. Meanwhile, research has indicated that the level of academic dishonestly has not changed with the prevalence of AI (Lee, et al, 2024) Yet the focus on student policing places instructors in an adversarial relationship with the students, instead of an instructive one. 

 

A Better Solution

 

Low-stakes assignments have the added benefit of deterring academic dishonesty.  Because there is less risk involved in the assignment, there is less incentive to cheat, which could bring about sever consequences.  Why take the risk, when there is so little to gain?

 

Low-stakes assignments, such as threaded assignments, thwart using AI that quickly generates content.  The low-stakes assignments act as scaffold that requires further reflection and meta-cognitive skills that are not easily replicated by the stochastic language models modern AI uses. This will force the would-be culprit to reflect and expend so much energy producing something to submit that it isn’t worth the effort to get whatever the AI model can produce.

 

When assignments:

•  offer less stress, 
• supply tools for the students to succeed, 
• clearly express expectations, and 
• encourage the student to take control of their learning, 

the students begin to see the value in what they are learning.  This dissuades the learner from cutting corners instead of easily going through the process.  Not only will this lower academic dishonesty in your classes, low stakes assignments will encourage more of your students to become engaged active learners.

 

Low-stakes assignments engage the students in the learning process.  The best way to eliminate academic dishonestly is to remove the incentive to cheat and be up-front about the rules.  Students are often ‘cheating’ because often they have not received adequate instruction and expectations (Waltzer, Bareket-Shavit & Dahl, 2023).  To solve this, explicitly state the acceptable level of AI usage and you will curb the level of unintended violations to your academic expectations. A group of scaffolded low stakes assignments, often scaffolded to create a large assignment, then undermines the pay-off from cheating.

References

Futterman, K (2024) Zeitgeist 6.0: Results of the Campus sixth-annual student-body survey. The Middlebury Campus. Dec 13.

Lee, V., Pope, D., Miles, S, and R. Zarate (2024) Cheating in the age of generative AI: A high school survey study of cheating behaviors before and after the release of ChatGPT. Computers and Education: Artificial Intelligence. Vol 7, December.

Losey, R. (2024) Stochastic Parrots: How to tell if something was written by an AI or a human? 

Mintz, S (2023) 10 Ways to Prevent Cheating. Inside Higher Ed. February 16.

Mollick, E (2023) Centaurs and Cyborgs on the Jagged Frontier: I think we have an answer on whether AIs will reshape work. One Useful Thing. Sep. 16.

Narayanan, A & S. Kapoor (2024) AI Snake Oil: What artificial Intelligence can do, what it can’t, and how to tell the difference. Princeton University Press: Princeton, NJ.

Waltzer, T., Bareket-Shavit, C., & Dahl, A. (2023). Teaching the What, Why, and How of Academic Integrity: Naturalistic Evidence from College Classrooms. Journal of College and Character, 24(3), 261–284.

Wehlburg, K (2021) Assessment design that supports authentic learning (and discourages cheating) Times Higher Education. Nov 24

Low-Stakes Assignments for Grading

Did you ever have a dream where you were back in school, you enter a class, and you realize you have a final exam on a topic that you have no idea what it is?  The pressure of exams is so great that it unconsciously affects us decades later.  In fact, high-stakes assignments and testing have been linking to increasing suicidal ideations (Wang, 2016), and higher suicide rates (Kapur, 2021; Singer, 2017). They have been connected to undermining educational goals, perpetrating inequalities, crating unequitable learning environments, and encouraging cheating both from students and educational actors, such as teachers, administrators, and even state officials (Nicols & Berliner, 2007). So then, why do we use high-stakes testing and assignments?  Tradition?

 

Low-stakes assignments when taken individually do not significantly impact a students’ grades. Their purpose is primarily to provide students with a performance indicator.  Students can then reflect on the areas in need of improvement and how to improve. The low-stakes assignment also provides assistive scaffolding by providing regular formative feedback that is frequent and timely (Kuh, Kinzie, Schuh, & Whitt, 2010). They work best when providing formative feedback, starting and continuing throughout the course.

 

Benefits of Low-Stakes Assignments

A few of the benefits of low-stakes assignments include:

• They provide feedback for instructors about how successful students are learning. This can be particularly effective in environments where it is hard to pick up on subtle clues of students struggling, such as in online or hybrid classes.
• Allowing instructors to direct students to resources if they need further assistance or support
• Early feedback opens up communication between students and their instructors, possibly increasing their likeliness to seek help when needed
• Allowing students to be active participants in the evaluation of their own learning
• Encouraging students and increasing the likelihood of their engagement and attendance

Many of these will rise your retention rates and help students succeed.

 

Examples of Low-Stakes Assignments

But what would a low-stakes assignment look like? Some examples of low-stakes assignments include:

•  Self-tests. (ungraded or low-points). These can even be automated with online testing so that it does not take any time in the classroom.  These can also be anonymized to give students comfort. Self-tests are particularly effective when combined with having…
• Multiple attempts (on questions or whole exam). This feature reduces test-anxiety and allows students to learn from their errors.  When feedback is given for each question, you will notice the best results. The knowledge of that they can take the exam another time also reduces the pressure to cheat (Wehlburg, 2021).
• Discussion/Collaboration: Students sharing their writing or thoughts with others and get feedback will assist their learning and meeting learning outcomes
• Multiple submissions of a paper. Feedback from a first submission with time to reflect and rewrite their paper allows students to hone their writing skills.  
• Reflective journaling. Writing self-reflective content both increases one’s meta-cognitive skills used for learning as well as better develops writing skills.  An added perk is that AI tools have a hard time replicating this type of writing as well.
•  A Threaded Assignment i.e., breaking down the assignment into several parts.  Individually, the grades or low, but collaboratively the project aggregates to a large assignment, such as a term paper. This technique often proves the scaffolds that help disenfranchised, or otherwise struggling, students succeed. A sample of deconstructing a large assignment into components would be making a thesis paper into the following smaller assignments:

• Thesis/Abstract
• Outline
• Annotated bibliography
•  1st draft
• Final draft

These are just a few examples; however, they offer an excellent opportunity for both you and your students to get needed feedback to help improve your course’s student success rate. These also help develop a grading system that can clearly show the steps necessary for mastering the meeting the learning outcomes of the course. 

References

Bayraktar, B (2021) Tip: Many Low Stakes Assignments. Tips for Teaching Professors, Apr 6.

Drabick D. A. G., Weisberg R., Paul L., Bubier J. L. (2007). Keeping it short and sweet: Brief, ungraded writing assignments facilitate learning. Teaching of Psychology, 34, 172–176.

Greenhalgh, S. (2016) The Hidden Cost of Asia’s High Test Scores. The Diplomat. Dec 9

Hale, M. (2018) Thwarting Plagiarism in the Humanities Classroom: Storyboards, Scaffolding, and a Death Fair. Journal of the Scholarship of Teaching and Learning, v18 n4 p86-110 Dec.

Kapur, M (2021) Student suicides put a spotlight on high-pressure exams during India’s pandemic. Quartz. Sept 17.

Kuh, G. D., Kinzie, J., Schuh, J.S., and Whitt, E.J. (2010). Student success in college: Creating conditions that matter. San Francisco, CA: Jossey-Bass.

Nicols, S. and D. Berliner (2007) Collateral Damage: how High-States Testing Corrupts America’s Schools. Harvard Education Press, Cambridge, MA.

Mintz, S (2023) 10 Ways to Prevent Cheating. Inside Higher Ed. February 16.

Singer, S (2017) Middle School Suicides Double as Common Core Testing Intensifies. Huffington Post. Aug 2.

Wang, Liang. (2016). The effect of high-stakes testing on suicidal ideation of teenagers with reference-dependent preferences. Journal of Population Economics. 29.

Warnock, Scott. (2013). Frequent, low-stakes grading: Assessment for communication, confidence. Faculty Focus. Madison, WI: Magna Publications. 

Wehlburg, K (2021) Assessment design that supports authentic learning (and discourages cheating) Times Higher Education. Nov 24.

Using Intelligent Agents and Gamification for Professional Development

 

Most every college online learning department in the US sparsely staffed given the tasks they is expected of them. While faculty are experts in their field of study, instructional design is separate field and it cannot be assumed that all faculty have this training.  This knowledge is critical for the professional development of the faculty and directly affects the accessibility of online course content as well as adopting best practices in teaching and learning, such as regularly reviewing online courses quality.  This become an added burden for understaffed department. Nevertheless, these are necessary are for meeting Middle States Accreditation (Standard III, section 4). 

To make this task tractable, a school using a LMS such as Brightspace, can create a non-termed ‘course’ where faculty can both learn what it is like to be a student in the system and participate in a self-paced training to improve the understanding of necessary topics.  By creating a communal professional development space, faculty can autonomous select topics to improve and break down silo’s with strategic online forums. The shared space can also act as repository of tools and OER content that faculty can easily access.

 In the case of SUNY Schenectady, a 5 module self-paced course allowed faculty to select between modules the content they wanted to first focus on.  In order to progress, certain requirements were necessary to advance further in the course.  This allowed modules to provide scaffolded activities.  The modules also provided a quantifiable space that required about the same about of effort to complete while each module illustrated Universal Design for Learning principles in an attempt to teach by example.

The true challenge to professional development is getting faculty to participate in the training.  To do this we had three incentives:

1. Learning outcomes for each module included building assets that the faculty could use in their courses.  Often these assets employed strategies that would increase student engagement and reduce faculty effort. 
2. Gamified modules offered badges for completing sections.   By design it encouraged or lead learners to produce more or complete the course
3. Certificates – were set up through an automated system (Awards).  Faculty would received personalized and dated certificates for not only completing the courses, but for completing each module.   This would allow for learners to use their work in each module to generate measured evidence (a certificate).  These illustrate a quantifiable amount of work  and the learners can share the certificates with their supervisors as evidence of the professional development in the annual review. For those without Adobe PDF Pro, a generic certificate can also work,
   
   

Automated Enrollment

Using Intelligent Agents in Brightspace, the system can identify all those with a specific roll, such as Faculty, and enroll them into a class.  With a little forethought, the school can enroll all employees in a “Staff” role that can be upgraded to “Faculty” if they teach.  This will allow all members of school to have access to the training area to promote topics with ubiquitous applications, such as “Accessibility”.  With the two roles, an Intelligent Agent can easily enroll all those working at the campus.   Naturally, this can be expanded with new modules and even become a single place for the school’s professional development

Principle of Engagement - Guideline 3 - Criterion 3

 

Universal Design for Learning

Principle of Engagement - Guideline 3 - Criterion 3

When developing a course using the third principle of Universal Design for Learning, there are three specific guidelines to assist us.  The third, Self-Regulation addresses maintaining focus and determination.

Criterion 3 of this guideline advocates that we develop self-assessment and reflection

Learners need to monitor their emotions and reactions accurately in order to develop their ability at self-regulation. The propensity and capacity for metacognition will vary greatly among learners.  As an instructor you should not be surprised to have students who may require considerable amounts of explicit instruction and modeling before they can self-assess effectively.  For some, merely recognizing that they are making progress can be highly motivating.  However, others may need more.  The inability to recognize one’s own progress can be a key demotivating factor.  Having multiple models and scaffolds of a variety of self-assessment techniques is important so learners can select the ones that will work best for themselves. This will increase learners’ awareness of their progress and how they may learn from their mistakes. The latter is critical, as recognizing errors as educational opportunities is critical for accelerating growth and success.  

Some strategies to meet this criterion include:

• Providing tools to chart or display data that marks improvement to assist learners to modify strategies that will aid in this success
• Providing ample activities that offer timely feedback that better frames the learners' progress
• Developing assignments that supply feedback and allow learners to reflect on the feedback and adapt new strategies based on their reflection.
• Supplying a developed gradebook, or center, where learners can view their progress relative to the course and have access to detailed feedback.  (When using any learning management system, such as Blackboard or Moodle, this is relatively easy because the feedback areas are already linked to the grade center)

By following these suggestions, your course will assist students in communicating and expressing their knowledge, as well as being in line with the Principle of Engagement in the Theory of Universal Design for Learning.

Principle of Engagement - Guideline 3 - Criterion 2

 

Universal Design for Learning

Principle of Engagement - Guideline 3 - Criterion 2

When developing a course using the third principle of Universal Design for Learning, there are three specific guidelines to assist us.  The third, Self-Regulation addresses maintaining focus and determination.

Criterion 2 of this guideline advocates that we facilitate personal coping skills and strategies.

Most learners will require more than just a model of self-regulatory skills. Scaffolding and sustained apprenticeships, possibly with mentors, contribute to the development of these strategies. Employing reminders, checklists, and process models can assist learners in adopting an adaptive strategy for managing their response to external events, such as social settings that may produce anxiety. This also applies to responses to internal events, such as for decreasing depression from facing the workload.

 
These strategies should offer flexible options to account for the individual variation in learners and accommodate the differences that variation brings. With these strategies, learners will be able to manage healthy responses to the stress of learning and possible setbacks, and eventually overcome these setbacks.

When addressing this, consider developing or providing scaffolds and feedback for:

• Managing frustration in not meeting goals or succeeding in tasks
• Seeking external support (does your campus have external services or could you embed them into your learning environment?)
• Developing coping controls to stress and setbacks
• Implementing exemplars to demonstrate effective coping skills
• Dispelling antiquated tropes about aptitude instead of viewing learning as improving (For example adopting the mindset of “How do I get better at Statistics?” instead of “I am not good at math”)

By following these suggestions, your course will assist students in communicating and expressing their knowledge, as well as being in line with the Principle of Engagement in the Theory of Universal Design for Learning.

Accessibility Checkers

Creating accessible content is easier now than ever before.  While there are several rules to learn and standards are regularly changing, such as WCAG standards, creating accessible content does not require a specialist. There are plenty of tools to assist you.  Most applications have accessibility checkers build right in.   These make producing accessible content easy.

We commonly make spell checking or grammar checking part of the process in creating a document.  Now it is time to include accessibility checking. 
 

You may notice that with so many accessibility checkers, sometimes they may differ in their analysis. Why should we pay attention to them?  Remember that accessibility standards are changing – they are evolving and regularly improving.  Algorithms can vary between programs and are trying to keep up.  The best course of action is to rely on any and all tools at your disposal.  This way you can work at having the most accessible content as possible.

Schools that take accessibility seriously should have at least three accessibility checkers at your disposal.

Available Accessibility Checkers

MS Word (or MS Office Apps)

Depending on the version, the accessibility checker can be in different locations.  It is most commonly on the “review ribbon”, but earlier versions have it under File.  Be sure to use it whenever you create a word document.

  

Ally

Those gauges next to content indicate the accessibility rating.  

Be sure to click on it and see why it is not dark green (or 100% accessible).  Ally will often direct you on how to improve your content.

Accessibility checker in the LSM HTML Editor

For example:
 Brightspace provide an accessibly checker in their HTML editor in the tools ribbon. The icon looks like an eye with a checkmark next to it. Naturally, if you hover the mouse over the tool buttons, you will see their names pop up. If you select the icon, it will scan the page, identify any issues, and offer advice.

It is important get acquainted with the accessibility checkers and incorporate it in your content creation routine.  It does not take much time to ensure that the content you create is accessible. In fact, the more you use the checkers, you will notice that you will be unconsciously adopting accessible practices when you produce content. This is a skill that will help you and others.

Principle of Engagement - Guideline 3 - Criterion 1

 

Universal Design for Learning

Principle of Engagement - Guideline 3 - Criterion 1

When developing a course using the third principle of Universal Design for Learning, there are three specific guidelines to assist us.  The third, Self-Regulation addresses maintaining focus and determination.

Criterion 1 of this guideline advocates that we promote expectations and attitudes that optimize motivation.

Everyone has intimate knowledge of what they find personally motivating and this is an important aspect to self-regulation.  Learners need to be able to set personal goals that they can reasonably obtain, as well as foster positive beliefs that they will be able to meet their goals.  Successfully accommodating this will require allowing for ways that learners can curtail frustration and eschew anxiety that many face while striving to meet their goals.  This will require multiple options for learners to help stay motivated.

To do this, you may consider:

• Providing guides, rubrics, prompts, and checklists to encourage increasing the length of on task orientation and elevating the frequency of self-reflection and self-reinforcements
   
• Create activities that support self-reflection and identifying, and assessing, personal goals
  
  
• Explicitly and implicitly promote the use of coaches, mentors, and tutors that help inculcate skills in goals setting that successfully account for both strengths and weaknesses.
   
• Encourage external success coaching and tutors for additional help, and promote the development of these services if your institution does not have them.

By following these suggestions, your course will assist students in communicating and expressing their knowledge, as well as being in line with the Principle of Engagement in the Theory of Universal Design for Learning.

Page Titles and Language Sets in Brightspace

The Problem

You have been working on ensuring your course has a perfect Ally Accessibility score and to your surprise you see a page that has an issue.  It reads:

• 96%: This HTML file does not have a language set.

If you explore the issues, you may notice that the page is also missing a title.  Both are critical for accessibility.  To your dismay, Ally states, “Guidance is not available yet”.

Figure 1

Guidance is Here

Luckily, we do not have to rely on Ally to solve this problem and you do not need to be an experienced HTML programmer either.  Any faculty can resolve this issue in the Brightspace editor.  You simply need to follow these steps:

1. Select Edit HTML to the page in question to open the editor. (See Figure 1)
2.  On the toolbar select the drop down from the Other Insert Options icon – it has a + sign on the button (See Figure 2)
3.  Select the Attributes option.
4. Select the “Page Attributes” Tab (See figure 3)
5. Give the page a meaningful title (if it doesn’t already have one)
6. Set the language set – “en’ for English (This is so much easier than adding <html lang="en"> to in the html code.)
7. Select Create.
8. Select the blue “Save and Close.”

That was it.  

Figure 2

You didn’t have to open the source code and drop in the appropriate code ( <html lang="en"> for the English language set).  Instead, you can quickly fix any problematic pages with these issues and ensure your content passes WCAG accessibility standards. 

Figure 3

Now that you know this, you can have perfect pages.

Principle of Engagement - Guideline 3 - Self-Regulation

 

Universal Design for Learning

Principle of Engagement - Guideline 3 - Self-Regulation

The third principle of UDL, "provide multiple means of engagement”, helps promote an inclusive learning environment on many levels.  When developing a course and focusing on this principle, there are three guidelines to follow.  They are:

1.  Recruiting Interest
2. Sustaining Effort and Persistence
3. Self-Regulation

We are now going to take a deeper look.

Self-Regulation is the ability to regulate emotions and motivations necessary to succeed at a task, such as learning. Naturally, developing and mastering this ability is important for all learners. While a learning environment can increase engagement by recruiting interest and sustaining effort and persistence, the ability to self-regulate, or to control one’s emotions and motivations so as to effectively cope and engage with the environment, is critical for learners’ success, both in and outside of the learning environment.


Many learners have developed these skills in primary school, however, there are still adult learners who struggle developing these skills. This is partially true due to most classrooms not explicitly addressing these skills and marginalized students finding this skill set inaccessible or imperceptible within an implicit curriculum. Regardless of the cause, designing a course to promote this skill, through prompts and modeling, helps level the field for everyone.


This approach provides alternatives to support learners with different aptitudes and prior experience to effectively manage their own engagement and affect. When adopting this approach, consider the following criteria:

1. Promote expectations and attitudes that optimize motivation
2. Facilitate personal coping skills and strategies. These strategies should offer flexible options to account for the individual variation
3.  Develop self-assessment and reflection. Learners need to monitor their emotions and reactions accurately in order to develop their ability at self-regulation

By promoting an inclusive strategy with multiple ways of promoting self—regulation, your courses will be more develop skills that will promote student success in the course and in life.

Principle of Engagement - Guideline 2 - Criterion 4

 

Universal Design for Learning

Principle of Engagement - Guideline 2 - Criterion 4

When developing a course using the third principle of Universal Design for Learning, there are three specific guidelines to assist us. The second, Sustaining Effort and Persistence addresses maintaining focus and determination.

Criterion 4 of this guideline advocates that we increase mastery-orientated feedback.

Feedback is critical for successful learning and is the fourth principle of Chickering and Gamson’s seven principles for good practice in undergraduate education.  When the feedback is relevant, accessible, constructive, and timely, the feedback is both more productive and critical for sustaining motivation and effort crucial for learning.

Mastery-oriented feedback guides learners towards master rather than a narrow and fixed view of performance and compliance.  It focuses on top tier learning in Blooms taxonomy while emphasizing the learners’ effort and practice as important factors for successful long-term habits and learning practices. This empowers learners with a sense of agency and treats learning as improving a skill, instead of a fixed target.  The latter notion often adopts the notion that some students, particularly those with disabilities, may be constrained from meeting these fixed goals, and thus impedes motivation to persist.

When trying to meet this criterion, consider:

• View learning as improving, and focus on effort, improvement, and achieving a standard. 
• Supplying feedback that promotes perseverance, the development of self-awareness, and encourages the use of strategies that will assist learning when they face challenges.
• Providing timely feedback
• Offering frequent and specific feedback
• Adopting strategies or models that will ensure that the feedback will be more substantive and informative, instead of comparative or competitive.
• Within your feedback, include how learners can incorporate the feedback to help identify patterns that promote errors so that they can self-correct in the future. 
• The feedback should also include positive strategies for further success. 

By following these suggestions, your course will assist students in communicating and expressing their knowledge, as well as being in line with the Principle of Engagement in the Theory of Universal Design for Learning.

References

Chickering, A & Z Gamson (1987) Seven Principles for Good Practice in Undergraduate Education. American Association for Higher Education, p 2-6.