Thursday, June 12, 2025

Micro-Feedback

 Data and Assessing Courses

When we teach, either online courses or in the classroom, gathering information for assessment is critical to improve what we do as educators.  Assessment is one of the aspects that educators often don’t do well.  Many times, course evaluation surveys are the extent of our attempts to assess whether we are meeting the courses goals. These surveys are distributed at the end of the course and if the instructor is lucky, the results may be available months later.  This process neglects the needs of the instructor by not affording them the information they need to make corrections and improve in real time.  Naturally, the students also suffer, as they will not benefit from real time corrections by the instructor.


The ADDIE model is a traditional instructional design methodology to help streamline the production of your course.  The name is an acronym for the 5 steps of the model:

  1. Analyze
  2. Design
  3. Develop
  4. Implement
  5. Evaluation

Evaluation is set at the end of the process, and while the process is supposed to be cyclical, often critiques of the process is that it is too time consuming.  Models like Rapid Instruction Design (RID) or Agile emphasize including evaluation throughout the stage.  Whether these critiques of the ADDIE model or if traditionally people have failed to apply the ADDIE model functionally at different levels is an interesting question, but will not directly help you improve you course now.

Micro-Feedback

Developing mechanisms to collect feedback throughout the course, or micro-feedback, and using it to improve your course on the fly can be very helpful.  By including short surveys at the module level (or smaller), instead of just one large survey at the end of the course, instructors can gain valuable insight that can help them rapidly adapt to their students’ needs.  This improvement will directly affect the students. Moreover, the adaptation can meet the specific needs of the students ad may vary the next time the course is taught.

Adopting a flexible and adaptive approach using micro-feedback gives you a greater understanding of your course.  Surveys can be short, and can include some reflective feedback from the student to give you the information you need to both better meet their needs and improve student success.  While it may seem difficult, adding a small (5 question) option survey can give you valuable insight on improving your courses.  You may find that the information can help you by giving you insight that could streamline your instruction and reduce your work by making it more effective.

Friday, April 18, 2025

AI and Computer Programming


Computer Science faculty have been dealing with AI tools and coding before the vast popularity of generative AI (Gen AI) in 2023. Since that popularity, there has been much attention to how AI will transform software development and coding.

Pros of AI Assisted Programming

Specifically, proponents of Gen AI identify key benefits that it offers. These include:
 
  • Decrease coding time: Programmers in code up to twice as fast using generative AI (Deniz et al, 2023).
  • Higher Satisfaction: 60-75% of programmers have reported that using AI felt more fulfilled with their job. Likewise, 73% stated that AI assisted their focus and 87% stated that they benefited from the reduced mental effort form repetitive tasks (Kalliamvakou,2024). The increase rate of adopting the suggestions from AI also correlates with the programmers perceived satisfaction (Ziegler et al, 2022).
 
However, it is important to note that perceived satisfaction from surveys may contain biases that is not the same as quantitative research on productivity and programmer focus (Moore, 2024b)


Cons of AI Assisted Programming

However, there are also significant costs that come with using gen AI to code. A few of these costs include:
  • Security: Programmers who use AI for assisting coding are more likely to introduce security vulnerabilities in their code (Perry et al, 2023). Those investing in repeated inquiries and examining the AI generated code could off-set this increase, but at a cost of time saved by using AI.
  • Productivity: There has been no productivity gain and potentially some serious downsides. First, programmers using gen AI had a 41% increase in their bug rate (Moore, 2024a). This creates a greater need to debug the code created.
  • Satisfaction and Burn-out: Controlled for sustained effort, the extended worktime outside of standard times shows that there was no reduction of the rate of burn-out from programmers when using AI. (Moore, 2024c)
These costs suggest that whatever perceived benefits of using Ai to code can be off-set by the increased time debugging and examining the code it generates.

The appealing nature of AI coding tools to create vast amounts of code poses long-term danger: as developers get accustomed to the perceived speed of the AI tools, they may gain false confidence and rely on them to program and review code more. This will perpetrate more bugs and will be amplified by code that is not documented. There will also be a skill loss in organizations.

Tips When Using AI Assisted Programming

If one is going to use Gen AI to assist with programming, it is important to understand its limitations. This entails:

  • Repeatedly examine the code for bugs and errors. Gen AI repeatedly produces code with errors. These errors do not reduce when increasing its prompts. In some cases, AI produced twice as many errors and took longer to patch errors (Jesse, 2023).
  • Do not rely on Gen AI for the most difficult programming issues. AI increasingly creates unsatisfactory code the harder the problem becomes. This is also the case when working with specific organizational content. Since the AI was not trained on the specific content, it regularly creates errors that prefer its training data and not solving the problem in within the organizational context (Moore, 2024b).
Given these needs, programmers need to leverage their skills to monitor code to reduce the error rates. Producing less bugs and security issues in the final product will be the hallmark of desired programmers. This will also leverage programming expertise against the speed of dubious code produced by Gen AI and ensure that skilled programmers will be more preferable than those that rely on Gen AI to produce their final product.

How Does that Affect Education?

If decisions are not made soon, educators will lose the ability to shape future programmers and face future challenges to come (Becker, et al, 2023). Responsible educators must remind their students of critical issues associated with coding with AI. Further they need to produce learners who can assess and reflect on Gen AI as a tool instead of a solution.

References

Becker, B, Denny, James, P, Finnie-Ansley, J, Luxton-Reilly, A., Prather, J, and E. Antonio Santos (2023). Programming Is Hard - Or at Least It Used to Be: Educational Opportunities and Challenges of AI Code Generation. In Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1 (SIGCSE 2023). Association for Computing Machinery, New York, NY, USA, 500–506.

Cui, Z, Demirer, M, Jaffe, S, Musolff, L, Peng, S, and T. Salz (2025) The Effects of Generative AI on High-Skilled Work: Evidence from Three Field Experiments with Software Developers Feb 10

Deniz B.K. Gnanasambandam, C, Harrysson, M, Hussin, A and S. Srivastava (2023) Unleashing developer productivity with generative AI. McKinsey Digital. June.

Jesse, K, Ahmed, T, Devanbu, P, & E. Morgan. (2023). Language Models and Simple, Stupid Bugs. Zenodo.  Feb. 25.  

Kalliamvakou, E (2024) Research: quantifying GitHub Copilot’s impact on developer productivity and happiness. GitHub.

Meyer, A, Fritz, T, Murphy, G, and T. Zimmermann. (2014). Software developers' perceptions of productivity. In Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering (FSE 2014). Association for Computing Machinery, New York, NY, USA, 19–29.

Perry, N, Srivatava, M, Kumar, D, and D, Boneh (2023) Do Users Write More Insecure Code with AI Assistants?. In Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security (CCS ’23), November 26–30, 2023, Copenhagen, Denmark. ACM, New York, NY, USA.

Ziegler, A, Kalliamvakou, E, Li, A, Rice,A, Rifkin,D, Simister, S, Sittampalam, G, and E.Aftandilian. (2022) Productivity Assessment of Neural Code Completion. In Proceedings of the 6th ACM SIGPLAN International Symposium on Machine Programming (MAPS ’22), June 13, 2022, San Diego, CA, USA. ACM, New York, NY, USA

Thursday, March 20, 2025

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 ChatGPTHowever, 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.



Wednesday, January 15, 2025

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


Wednesday, November 27, 2024

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.

Tuesday, May 21, 2024

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


Wednesday, March 20, 2024

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.

Micro-Feedback

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