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LeMoRe Examples:
Open Learner Models

This page shows examples of open learner model screens. Each gives a brief description (up to 50 words), and a key reference for further information. Author contact information can be found on the LeMoRe Members page.

We thank those who have contributed their open learner models to this page. New examples are always welcome.

Paul Brna, Susan Bull, Vania Dimitrova.

QuizGuide

QuizGuide is an adaptive hypermedia service presenting students with the state of their user model to support navigation through the learning content.

Reference

Brusilovsky, P. & Sosnovsky, S. (2005). Engaging Students to Work with Self-Assessment Questions: A study of two approaches, Proceedings of 10th Annual Conference on Innovation and Technology in Computer Science Education, ACM Press, 251-255.

C-POLMILE

C-POLMILE can be used on the desktop PC or handheld computer. It requires an editable learner model in order that students can easily update it if they use different versions of the system without having synchronised their devices. The display uses extended skill meters, percentages and text descriptions of misconceptions.

Reference

Bull, S. & McEvoy, A.T. (2003). An Intelligent Learning Environment with an Open Learner Model for the Desktop PC and Pocket PC, in U. Hoppe, F. Verdejo & J. kay (eds), Artificial Intelligence in Education, IOS Press, Amsterdam, 389-391.

Mr Collins

Mr Collins (COLLaboratively maintained, INSpectable learner model) aims to encourage greater learner involvement in the construction and repair of the learner model in order to gain a more accurate model, while at the same time promoting learner reflection as a result of this interactive, negotiated learner modelling process.

Reference

Bull, S. & Pain, H. (1995). 'Did I Say What I Think I Said, And Do You Agree With Me?': Inspecting and Questioning the Student Model, in J. Greer (ed), Proceedings of World Conference on Artificial Intelligence and Education, AACE, Charlottesville VA, 501-508.

STyLE-OLM

STyLE-OLM (Scientific Terminology Learning Environment - Open Learner Model) uses conceptual graphs and dialogue games to involve the learner in interactive open learner modelling.

Reference

Dimitrova, V. (2003). STyLE-OLM: Interactive Open learner Modelling, International Journal of Artificial Intelligence in Education 13(1), 35-78.

SIV

SIV consists primarily of a visualisation that provides an overview of the components in the learner model. It uses semantic inference to overcome problems with granularity and data availability. Learners can inspect specific concepts and see the evidence sources contributing to its score.

Reference

Kay, J., Lum, A. (2005). Exploiting Readily Available Web Data for Scrutable Student Models, 12th International Conference on Artificial Intelligence in Education, Amsterdam, Netherlands, 338-345.

Haptic Learner Model

The learner model can be viewed in textual form (left) or using a force-feedback device for the haptic learner model (right). Learners physically interact with the ‘concept spheres’. Known concepts (green) feel hard; less well known concepts (orange) feel softer; misconceptions (red) feel soft and sticky.

Reference

Lloyd, T. & Bull, S. (2006). A Haptic Learner Model, International Journal of Continuing Engineering Education and Lifelong Learning 16(1-2), 137-149.

Flexi-OLM

Flexi-OLM offers the user a choice of seven views of their learner model contents: alphabetical index, list ranked according to knowledge, concept map, hierarchical structure grouping related concepts, pre-requisites structure, lecture structure, textual summary.

Reference

Mabbott, A. & Bull, S. (2004). Alternative Views on Knowledge: Presentation of Open Learner Models, in J.C. Lester, R.M. Vicari & F. Paraguacu (eds), Intelligent Tutoring Systems: 7th International Conference, Springer-Verlag, Berlin Heidelberg, 689-698.

CourseVis

CourseVis creates graphical representations of tracking data from a Web-based Course Management System and generates graphical representations that can be explored by instructors to examine social, cognitive, and behavioural aspects of distance students.

Reference

Mazza, R. & Dimitrova, V. (2004). Visualising Student Tracking Data to Support Instructors in Web-Based Distance Education, 13th International World Wide Web Conference - Alternate Educational Track, 154-161.

SQL-Tutor

Constraint-based tutors open the student model as skillometers. Evaluation studies show that even such simple open student models do support students in self-assessment and reflection.

Reference

Mitrovic, A. & Martin, B. (2002). Evaluating the Effects of Open Student Models on learning, P. de Bra, P. Brusilovsky & R. Conejo (eds), Proceedings of 2nd International Conference on Adaptive Hypermedia and Adaptive Web-based Systems, Springer-Verlag, Berlin Heidelberg, 296-305.

LOZ (Learn Object-Z)

LOZ is a CBL system for learning Object-Z. Fuzzy rules, which reflect a human tutor’s decision making processes, are used for selecting pedagogical actions. Opening a fuzzy learner model is easy as it uses every-day terms and real-world reasoning processes. Moreover, the scaffolding strategy used for mentoring is also open.

Reference

Mohanarajah, S., Kemp, R.H. & Kemp, E. (2005). Opening a Fuzzy Learner Model, Proceedings of Workshop on Learner Modelling for Reflection, International Conference on Artificial Intelligence in Education, Amsterdam, Netherlands, 62-71.

INSPIRE

INSPIRE allows learners to access their model, reflect upon its contents (learning style, knowledge level of the domain concepts), and change them in order to guide instructional decisions. Moreover, learners are informed about the way the system estimates their individual characteristics, and the impact of these characteristics on system functionality.

Reference

Papanikolaou K., Grigoriadou M., Kornilakis H. and Magoulas G.D. (2003). Personalising the Interaction in a Web-based Educational Hypermedia System: The Case of INSPIRE, User-Modeling and User-Adapted Interaction 13(3), 213-267.

WILL Tools

The Will Tools comprise: Willow, an automatic and adaptive free-text scoring system; Willed, an authoring tool; Willoc, a configuration tool; Willov, a conceptual model viewer. Willow displays students’ conceptual models in several formats; Willov displays the models to teachers. The models are automatically generated from free-text answers provided to Willow.

Reference

Perez-Marin, D.; Pascual-Nieto, I. & Rodriguez, P. (2009). Adaptive Computer Assisted Assessment, in Fu Lee Wang, Joseph Fong & Reggie Kwan (eds), Handbook of Research on Hybrid Learning Models: Advanced Tools, Technologies and Applications, Information Science Reference (antes Idea Group Reference), Portugal, [accepted for publication].

DynMap+

DynMap+ (Dynamic visualization of Open Student Models thorough Concept Maps) allows the inspection of Individual and Group Student Models coming from different sources. Several visual mechanisms are used to open those models. It opens not only the last state of the models but also the evolution through the learning sessions.

Reference

Rueda, U., Larrañaga, M., Arruarte, A. & Elorriaga, J. A. (2006). DynMap+: A Concept Mapping Approach to Visualize Group Student Models, in W. Nejdl & K. Tochtermann (eds), Innovative Approaches for Learning and Knowledge Sharing, First European Conference on Technology Enhanced Learning, Springer-Verlag, Berlin Heidelberg, 383-397.

xOLM

The xOLM allows learners to explore a dynamic portrait of their abilities on a number of inter-connected levels (competency, motivation and affect, metacognition) and its supporting evidence. This approach is based on a Toulmin-like argumentation structure together with a form of data fusion based on an adaptation of Dempster-Shafer Theory.

Reference

Van Labeke, N., Brna, P. & Morales, R. (2007). Opening up the Interpretation Process in an Open Learner Model, International Journal of Artificial Intelligence in Education 17(3), 305-338.

ViSMod

ViSMod (Visualization of Bayesian Student Models) is designed to help students and their teachers to interact with Bayesian student models. Bayesian student models are maps containing information about cognitive and social aspects of the student. These maps can be explored and annotated by students and teachers during the learning process.

Reference

Zapata-Rivera, J.D. & Greer, J. (2004). Interacting with Inspectable Bayesian Student Models, International Journal of Artificial Intelligence in Education 14(2), 127-163.

To add a screen of your open learner model, please email s.bull@bham.ac.uk.
We will need a short description of the open learner model (maximum 50 words), one image and one reference.