1. Learning Analytics isn’t new Ways in which we might build on the long history of adaptive learning systems within contemporary online learning design Professor Barney Dalgarno uImagine Digital Learning Innovation Laboratory Charles Sturt University

2. Adaptive learning: teacher versus system Levels of system adaptivity Branching versus AI based techniques Overview of AI based techniques (intelligent tutoring systems including cognitive tutors, machine learning) Expectations of learner control and so guidance and feedback rather than content generation Can we automate guidance and feedback across content domains using machine learning? Overview

3. Analytics to inform teacher decision making: Teacher controlled adaptive learning Analytics used as input to learning system System controlled or supported adaptive learning My focus in this presentation Adaptive learning

4. Feedback on answers to discrete problems/questions Branching Adaptive release of content Adaptive content generation Adaptive feedback Problem solving guidance Learning strategy guidance Levels of system adaptivity Basic authoring and scripting Artificial intelligence (AI) based techniques (my focus here)

5. Intelligent tutoring systems (also referred to as cognitive tutors) Broader applications of machine learning algorithms AI Based Techniques

6. Architecture of an Intelligent Tutoring System Alley (2004) Student Interface Module Pedagogical Module Domain Module Student Module

7. Work flow of an Intelligent Tutoring System Koedinger et al. (2013)

8. Expectations of learner control over sequence and selection of content (underpinnings in adult learning principles and constructivist principles of learning) So: Containing learning within a system controlled content delivery system is rarely desirable (ie. the outer loop in an ITS) More important is Cognitive (content based) and Metacognitive (strategy based) guidance and feedback during learner controlled activities (ie. the inner loop) The higher education context

9. Examples of feedback and guidance  Problem solving strategy: global warming simulator (Dalgarno et al., 2014)  Procedural technique: ear surgery simulator (O’Leary et al. (2008)  Learning strategy: ‘At risk’ systems

10. All required analysis of historical data in order to identify characteristics or signatures of successful and unsuccessful strategies This analysis required statistical expertise which is costly The analysis was domain, context or problem specific So: is there a way to have the system provide this kind of feedback based on dynamic analysis of student learning behaviours and outcomes? Key limitation of these examples

11. Koedinger et al. (2013) note that: most intelligent tutoring systems (ITS) have been built through extensive knowledge engineering, and ideally cognitive task analysis, to develop models of student and expert skill and performance. But describe new potential for: data-driven symbolic and/or statistical machine learning approaches for automated or semi-automated development of the key components and functionalities of intelligent tutoring systems. The potential of machine learning within adaptive learning systems

12. Developing cognitive models by recording the actions of expert teachers tutoring a simulated student Dynamic generation of guidance (hints) based on the theoretical solution space and the ideal learner action at any point in time Data driven generation of algorithms to evaluate and provide feedback on student actions in complex problem domains Example early applications of machine learning techniques

13. System supported adaptive learning has been around for a long time In higher education we are only scratching the surface in terms of the techniques that we are using Intelligent tutoring systems are likely to be of value for well contained content domains (e.g. first year STEM) Research in machine learning has promise in helping to (affordably) address the goal of dynamic feedback and guidance for students while undertaking open ended tasks in complex domains Conclusion

14. Ally, M. (2004). Designing Distributed Environments with Intelligent Software Agents, Idea Group Publishing. Dalgarno, B., Kennedy, G., & Bennett, S. (2014). The impact of students’ exploration strategies on discovery learning using computer-based simulations. Educational Media International, 51(4), 310-329. Koedinger, K. R., Brunskill, E., Baker, R. S., McLaughlin, E. A., & Stamper, J. (2013). New potentials for data-driven intelligent tutoring system development and optimization. AI Magazine, 34(3), 27-41. O'Leary, S. J., Hutchins, M. A., Stevenson, D. R., Gunn, C., Krumpholz, A., Kennedy, G.,... & Pyman, B. (2008). Validation of a networked virtual reality simulation of temporal bone surgery. The Laryngoscope, 118(6), 1040-1046. References