LiftUpp: Support to Develop Learner Performance

The last two decades have seen enormous progress in both theories and technology to support learner progress. However, many of the Artificial Intelligence in Education (AIED) techniques are difficult to apply in workplace-based educational settings, such as dentistry. Such settings put high demands on e-infrastructure, because they require intelligent systems that can be used in the workplace every day, and can also fuse many different forms of assessment data together. In addition, such systems should be able to enhance student development through personalised real time feedback (in dentistry education, for example, from both staff and patients) to drive learner self-reflection. Moreover, the information these systems provide must be reliable to facilitate defensible decisions over individual student progress to protect the public [2].

[1]  Kenneth R. Koedinger,et al.  Individualized Bayesian Knowledge Tracing Models , 2013, AIED.

[2]  Ryan Shaun Joazeiro de Baker,et al.  New Potentials for Data-Driven Intelligent Tutoring System Development and Optimization , 2013, AI Mag..

[3]  Albert T. Corbett,et al.  Cognitive Tutor: Applied research in mathematics education , 2007, Psychonomic bulletin & review.

[4]  Tiffany Barnes,et al.  Automatic Hint Generation for Logic Proof Tutoring Using Historical Data , 2010, J. Educ. Technol. Soc..

[5]  Thomas L. Griffiths,et al.  Faster Teaching by POMDP Planning , 2011, AIED.

[6]  Leonidas J. Guibas,et al.  Deep Knowledge Tracing , 2015, NIPS.

[7]  Julita Vassileva,et al.  Active Learner Modelling , 2000, Intelligent Tutoring Systems.

[8]  L. Dawson,et al.  Calling for a re‐evaluation of the data required to credibly demonstrate a dental student is safe and ready to practice , 2016, European journal of dental education : official journal of the Association for Dental Education in Europe.

[9]  Helen E. Higson,et al.  Graduate Employability, 'Soft Skills' Versus 'Hard' Business Knowledge: A European Study , 2008 .

[10]  C. V. D. van der Vleuten The assessment of professional competence: Developments, research and practical implications. , 1996, Advances in health sciences education : theory and practice.

[11]  Kurt VanLehn,et al.  The Behavior of Tutoring Systems , 2006, Int. J. Artif. Intell. Educ..

[12]  G. Miller The assessment of clinical skills/competence/performance , 1990, Academic medicine : journal of the Association of American Medical Colleges.

[13]  Jim Crossley,et al.  Good questions, good answers: construct alignment improves the performance of workplace‐based assessment scales , 2011, Medical education.

[14]  John R. Anderson,et al.  Knowledge tracing: Modeling the acquisition of procedural knowledge , 2005, User Modeling and User-Adapted Interaction.

[15]  M. Govaerts,et al.  Validity in work‐based assessment: expanding our horizons , 2013, Medical education.

[16]  C. Lebiere,et al.  The Atomic Components of Thought , 1998 .