Modelling routes towards learning goals

Citation: Tattersall, C., Janssen, J., van den Berg, B., & Koper, R. (2006). Modelling routes towards learning goals. Campus Wide Information Systems, 23(5), 312-324.The traditional notion of the curriculum as a fixed list of topics to be studied sequentially is undergoing change informed by constructivist theories. However, abandoning the notion of the curriculum as a study plan raises the need to help learners in understanding both where they stand with respect to their desired competences and how these competences can be attained. This article presents a formal model for the description of routes to learning goals, designed to underpin guidance support systems for learners. The article compares the model to other work in the area, illustrates its application with a number of case studies and concludes with a discussion of the broader e-learning infrastructure required in implementing the approach

Studieadvies op basis van zelforganisatie: Dynamische wegwijzers in een veranderend onderwijslandschap

Van den Berg, B., Tattersall, C., Janssen, J., Van Es, R., & Koper, R. (2006). Studieadvies op basis van zelforganisatie: Dynamische wegwijzers in een veranderend onderwijslandschap. Unpublished manuscriptStudenten kunnen tegenwoordig kiezen uit een steeds groter aanbod van cursussen en trainingen, die vaak in de vorm van losse modules op verschillende manieren aan elkaar geschakeld kunnen worden. Dit biedt veel voordelen maar vormt ook de basis voor een keuzeprobleem: wat is de meest effectieve weg naar mijn doel? Dit artikel beschrijft de software architectuur die is ontworpen om Life-Long-Learners te ondersteunen bij het kiezen van hun leerpad in een leernetwerk. Deze architectuur sluit aan bij theorieën over zelforganisatie en indirecte sociale communicatie. De architectuur omvat de opslag, verwerking en presentatie van collectief studiegedrag en verschaft informatie over succesvolle leerpaden van medestudenten aan studenten die een vervolgkeuze voor hun studie maken. Er wordt een implementatie beschreven van een leernetwerk en verslag gedaan van de uitkomsten van een experiment met dit prototype. De resultaten leiden tot de conclusie dat er door het geven van een advies convergentie van leerpaden optreedt en dat de studenten die een studieadvies krijgen effectiever zijn

Using IMS Learning Design to Model Curricula

Tattersall, C., Janssen, J., van den Berg, B., & Koper, R. (2006). Using IMS Learning Design to model curricula. In R. Koper & K. Stefanov (Eds.). Proceedings of International Workshop in Learning Networks for Lifelong Competence Development (pp. 37-42). March, 30-31, 2006, Sofia, Bulgaria: TENCompetence Conference. Retrieved June 30th, 2006, from http://dspace.learningnetworks.orgThe traditional notion of the curriculum as a fixed list of topics to be studied sequentially is under strain as pressure for flexibility in education increases. However, curriculum flexibility can lead to curriculum complexity, hindering learners in the development of their competences. This article presents a formal model for the description of curricula, designed to underpin guidance support systems for learners. The article compares the model to other work in the area, illustrates its application with a number of case studies and concludes with a discussion of the broader e-learning infrastructure required in implementing the approach.This work has been sponsored by the EU project TENCompetenc

Learners’ evaluation of a navigation support tool in distance education

Bolman, C., Tattersall, C., Waterink, W., Janssen, J., Van den Berg, B., Van Es, R., et al. (2007). Learner’s evaluation of a navigation support tool in distance education. Journal of Computer Assisted Learning, 23 (5), 384-392.This article investigates the usability of a navigation support tool developed to guide learners in a lifelong learning environment. The navigation support tool generates advice on the best next step to take in an e-learning course following completion of a course module, and was evaluated in a largescale experimental study based around an online Internet skills course. This article follows on from the experimental study and examines learners’ evaluation of the navigational support. It uses automatically generated logs of completion of course modules and learners’ answers to online questionnaires to give insight into learners’ evaluation, learners’ adherence to the advice and selfefficacy information. The article describes the theoretical underpinnings of the work, the experimental setup and results, and draws conclusions to refine the navigation tool and to increase adherence to the advice

Swarm-based wayfinding support in open and distance learning

Please refer to the original source: Tattersall, C. Manderveld, J., Van den Berg, B., Van Es, R., Janssen, J., & Koper, R. (2005). Swarm-based wayfinding support in open and distance learning. In Alkhalifa, E.M. (Ed). Cognitively Informed Systems: Utilizing Practical Approaches to Enrich Information Presentation and Transfer. Information Science Publishing, USA. (pp. 166-183). [http://www.silvertair.com/CIS/Contents.htm] OR Tattersall, C. Manderveld, J., Van den Berg, B., Van Es, R., Janssen, J., & Koper, R. (2008). Swarm-based Wayfinding Support in Open and Distance Learning. In Sugumaran, V. (Ed). Intelligent Information Technologies: Concepts, Methodologies, Tools, and Applications. Information Science Reference, Hershey New York. (pp 846-857).Open and Distance Learning (ODL) gives learners freedom of time, place and pace of study, putting learner self-direction centre-stage. However, increased responsibility should not come at the price of over-burdening or abandonment of learners as they progress along their learning journey. This paper introduces an approach to wayfinding support for distance learners based on self-organisation theory. It describes an architecture which supports the recording, processing and presentation of collective learner behaviour designed to create a feedback loop informing learners of successful paths towards the attainment of learning goals. The approach is presented as an alternative to methods of achieving adaptation in hypermedia-based learning environments which involve learner modelling

Swarm-based Sequencing Recommendations in E-learning

To be presented at the International Workshop on Recommender Agents and Adaptive Web-based Systems (RAAWS 2005) held in conjunction with the Intelligent Systems Design and Applications 2005 Conference (ISDA 2005), Wroclaw, Poland, September 8-10, 2005. Proceedings 5th International Conference on Intelligent Systems Design and Applications, (Eds) Kwasnicka, H. & Paprzycki, M., IEEE Computer Society, 2005, pp.488-493Open and distance Learning (ODL) gives learners freedom of time, place and pace of study, putting learner self-direction centre-stage. However, increased responsibility should not come at the price of over-burdening or abandonment of learners as they progress along their learning journey. This paper introduces an approach to recommending the sequencing of e-learning modules for distance learners based on self-organisation theory. It describes an architecture which supports the recording, processing and presentation of collective learner behaviour designed to create a feedback loop informing learners of successful paths towards the attainment of learning goals. The article includes initial results from a large-scale experiment designed to validate the approach

Combining Social- and Information-based Approaches for Personalised Recommendation on Sequencing Learning Activities

Lifelong learners who assign learning activities (from multiple sources) to attain certain learning goals throughout their lives need to know which learning activities are (most) suitable and in which sequence these should be performed. Learners need support in this way finding process (selection and sequencing), and we argue this could be provided by using personalised recommender systems. To enable personalisation, collaborative filtering could use information about learners and learning activities, since their alignment contributes to learning efficiency. A model for way finding has been developed that presents personalised recommendations in relation to information about learning goals, learning activities and learners. A personalised recommender system has been developed accordingly, and recommends learners on the best next learning activities. Both model and system combine social-based (i.e., completion data from other learners) and information-based (i.e., metadata from learner profiles and learning activities) approaches to recommend the best next learning activity to be completed