Break Through Tech Branding Assistant - Proscribed Answers

Break Through Tech Brand Assistant - Open Answers

Learning about others, decision making, and autonomous features

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Learning about others, decision making, and autonomous features

Reflection Option #1:

Referring to the assigned articles for this week, as well as past readings, what are the different ways that learning analytics/educational data mining in relation to AI can: 
  1. assist individual self-regulatory learning, 
  2. facilitate successful collaborative learning
  3. assess the state and flow of a classroom in real-time.
There has been an idea that big data from data mining will enable researchers to build intelligent tutoring systems. This essentially means that we would be able to "develop rich student models that can be used...[for] automated personalization" for things like automated interventions (Baker, 2016, p. 604-605). 

Currently, there exists a wide array of systems that can provide step-by-step feedback on student activities or emulate complex student-tutor relationships. However, few of these systems are used at scale. Instead, the systems that are used at scale tend towards simple architecture or simple student models. 

This could possibly be due to some of the obstacles to creating these large scale implementations.  Baker (2016) lists three flaws of automated interventions, including 1) the time it takes to author them, 2) their lack of flexibility/adaptability, 3) the amount of upkeep they require to stay relevant. 

So how can learning analytics/educational data mining assist individual self-regulatory learning?

Baker (2016) proposes that we need "stupid tutoring systems, and intelligent humans" (p. 603). This would equate to using our findings from learning analytics to build better-designed online learning systems. Instead of integrating data mining takeaways into systems to create automatic interventions, we would use data mining or learning analytics as research tools that help instructors or designers create informed solutions to problems. In this way, designers and instructors would be better informed when creating online lessons or delivering interventions. 

How can learning analytics/educational data mining facilitate successful collaborative learning?

Some systems are able to help instructors monitor collaborative activities virtually or "take real-time action to improve the quality of collaborative discussions" (Baker, 2016, p. 606). Tissenbaum & Slotta (2019) discuss how teachers were able to facilitate group discussion, help students make connections and move past conflicts, and provide timely feedback, by utilizing a system that was able to monitor student interactions in real-time.  By monitoring student interactions in real-time, teachers were able to create student groups, provide timely intervention, and deliver effective feedback. 

How can learning analytics/educational data mining assess the state and flow of a classroom in real-time?

We all know that teachers have a lot on their plates. Well, data mining and learning analytics can help create more informed educational ecosystems to ensure that everyone in a student's life is informed on their state of learning. Specifically, Baker (2016) discusses the use of learning analytics to report on a student's state to diverse stakeholders, such as teachers, parents, or even the learner themselves. This ensures that the student is supported throughout the tenure of their education. Such systems may be implemented as early-warning systems or aids to monitor student states of learning and help instructors modify their content instruction or anticipate obstacles to learning. 

Tissenbaum & Slotta (2019) also describe how systems can be developed to monitor a classroom in real-time, in order to better facilitate the flow of class activities. The tablet the article describes is supported by data mining and software agents and helps to "redu[ce] the teacher's orchestration load and suppor[t] him as a wandering facilitator (Tissenbaum & Slotta, 2019, p. 345). 

References

Baker, R. S. (2016). Stupid tutoring systems, intelligent humans. International Journal of Artificial Intelligence in Education, 26(2), 600-614.

Tissenbaum, M., & Slotta, J. (2019). Supporting classroom orchestration with real-time feedback: A role for teacher dashboards and real-time agents. International Journal of Computer-Supported Collaborative Learning, 14(3), 325-351.

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