Musings on intelligent computer-assisted instruction

  My focus for blog reflection this week is exploring the different ways that learning analytics/educational data mining can   a) assist individual self-regulating learning    As described by Heffernan (2014), the ASSISTments ecosystem is a powerful platform that uses data mining to "assist as it assesses", which gives it the ability to provide feedback in real time, as students need it. As a platform, it can quickly deploy a wide range of content and experiments, based on student performance. The platform allows for easy construction of scaffolding questions, which helps support student problem solving, such as breaking a complex topic into smaller steps. For example, geometry scaffolding questions might include questions on each of the following: congruence, perimeter, equation solving and finally the substitution (p 475). While working through these problems, students would receive hints and feedback to facilitate the self-regulated learning. Over the course o...

Per your suggestion, I chose to have a "chat" with Kuki , a chatbot that has been touted as the closest thing to a "human" in terms of the Turing Test. After some exchanges, I am not so sure ... I initiated the conversation by saying I was doing ok but nervous about the election. Perhaps this compounded statement of how I am "good", but ... kind of lost the chatbot (I lost it on the hello). It seemed to just grab a first word, and ignore the remainder of my statement. But Kuki is the first to tell you, however that its vocabulary level is very, very high. Probing a bit further, I wanted to see if Kuki had a personality, or a notion of one anyway, Here's how that went: So, not so much, it turns out, but we were fast friends nonetheless. And apparently had even met before (I don't recall... is that possible?). So, I should also add that I don't engage in chatbot exchanges, so I tried a different track of conversation. I asked about trees. So, ok....

Forgive me for not putting forth the McLaren article as my greatest source of inspiration this far this semester ... that honor right now goes to Koedinger and his exploration of feedback, hints and the Assistance Dilemma in Experiments with Cognitive Tutors (2007). Koedinger rightly points out that understanding more about using feedback most effectively not only has significant implications for "instructional interventions in e-learning" in general. As so many aspects of instructional design, I increasingly appreciate the high level of orchestration of feedback, timing, just-in-time assistance and scaffolding that is characteristic of deep learning experience. And as Koedinger so aptly points out, it is not just the provision of feedback but potentially the withholding of it as well. In fact, that feedback is so richly variable that--beyond the exploration of cognitive tutors, it begs for an inquiry into how variable learners' needs are as well. In other words, the cha...

I s it possible that if McLaren, Scheuer, & Miksáko had been aware of the Explanatory Learner Model, it would have influenced their research model? Possibly, but it certainly should have, for a few reasons. First, McLaren et al put forth a model that placed the emphasis on prediction over the learner, and what could not be predicted was eliminated from Argunaut, even if those eliminations were intrinsic or essential to student discussions. "Explanatory models do not just provide accurate prediction, but also offer actionable insights that may better advance both learning science and educational practice" (Rosé, et al., p 2943); to do so, they bring together an interdisciplinary team that spans the engineering, learning sciences and technology with an eye on the learner, learner process and instructional context.  Second, "if AI-based analytics are to have an impact on education, developers must attend to the human interaction and social context of using technology...

A brief comparison of cognitive tutors and cognitive assistants In response to the reflection prompt for this week, I am focusing on the comparison between a cognitive tutor (Koedinger et al 2007) and a cognitive assistant (Le et al, 2018). Both are highly interactive learning tutors/agents but are starkly different in their designs and interaction capabilities, as well as their underlying design and theoretical rationales. As described by Koedinger et al (2007), a cognitive tutor (CT) is domain-specific and offers a "rich, problem-solving environment with a variety of problem-solving tools and presents authentic problem-solving scenarios for the student to solve" (p 244). The modus operandi of this graphical interface is a thoughtful combination of feedback (and its timing), assessment, and forced navigation. The feedback is at a granular-level, that it, it is delivered at each step of the problem-solving process, rather than at the end of the process, and can be delivered a...

Prompt: Below you see several (6-7) artifacts that can be pedagogical agents or intelligent computer assisted instructional systems, each at the different stages on the uncanny valley curve. How do you think EACH of these agents/systems will differ on  how the student will interact with the agent/system (and why)? what the agent/system may be capable of when it comes to evaluating/assessing student's learning (and why)?  for what kind of learning would such agent/system be good for (and why)?  Please reflect on two or more of the required readings from this week or (past readings) to justify/support your answer. You can answer each separately or all together in one paragraph.  Hal (2001: A Space Odyssey) It's been a long time since I have seen this movie (!), but Hal is a classic example of feedback delivered by a Voice of God (VoG) agent as described by Goldberg & Cannon-Bowers (2015), a disembodied voice that could interact with a learner in a number of ways (a...