@conference {2651, title = {Adaptive Item and Feedback Selection in Personalized Learning with a Network Approach}, booktitle = {IACAT 2017 Conference}, year = {2017}, month = {08/2017}, publisher = {Niigata Seiryo University}, organization = {Niigata Seiryo University}, address = {Niigata, Japan}, abstract = {

Personalized learning is a term used to describe educational systems that adapt student-specific curriculum sequencing, pacing, and presentation based on their unique backgrounds, knowledge, preferences, interests, and learning goals. (Chen, 2008; Netcoh, 2016). The technological approach to personalized learning provides data-driven models to incorporate these adaptations automatically. Examples of applications include online learning systems, educational games, and revision-aid systems. In this study we introduce Bayesian networks as a methodology to implement an adaptive framework within a personalized learning environment. Existing ideas from Computerized Adaptive Testing (CAT) with Item Response Theory (IRT), where choices about content provision are based on maximizing information, are related to the goals of personalized learning environments. Personalized learning entails other goals besides efficient ability estimation by maximizing information, such as an adaptive configuration of preferences and feedback to the student. These considerations will be discussed and their application in networks will be illustrated.

Adaptivity in Personalized Learning.In standard CAT\’s there is a focus on selecting items that provide maximum information about the ability of an individual at a certain point in time (Van der Linden \& Glas, 2000). When learning is the main goal of testing, alternative adaptive item selection methods were explored by Eggen (2012). The adaptive choices made in personalized learning applications require additional adaptivity with respect to the following aspects; the moment of feedback, the kind of feedback, and the possibility for students to actively influence the learning process.

Bayesian Networks and Personalized Learning.Personalized learning aims at constructing a framework to incorporate all the aspects mentioned above. Therefore, the goal of this framework is not only to focus on retrieving ability estimates by choosing items on maximum information, but also to construct a framework that allows for these other factors to play a role. Plajner and Vomlel (2016) have already applied Bayesian Networks to adaptive testing, selecting items with help of entropy reduction. Almond et al. (2015) provide a reference work on Bayesian Networks in Educational Assessment. Both acknowledge the potential of the method in terms of features such as modularity options to build finer-grained models. IRT does not allow to model sub-skills very easily and to gather information on fine-grained level, due to its dependency on the assumption of generally one underlying trait. The local independence assumption in IRT implies being interested in mainly the student\’s overall ability on the subject of interest. When the goal is to improve student\’s learning, we are not just interested in efficiently coming to their test score on a global subject. One wants a model that is able to map\ educational problems and talents in detail over the whole educational program, while allowing for dependency between items. The moment in time can influence topics to be better mastered than others, and this is exactly what we can to get out of a model. The possibility to model flexible structures, estimate abilities on a very detailed level for sub-skills and to easily incorporate other variables such as feedback in Bayesian Networks makes it a very promising method for making adaptive choices in personalized learning. It is shown in this research how item and feedback selection can be performed with help of the promising Bayesian Networks. A student involvement possibility is also introduced and evaluated.

References

Almond, R. G., Mislevy, R. J., Steinberg, L. S., Yan, D., \& Williamson, D. M. (2015). Bayesian Networks in Educational Assessment. Test. New York: Springer Science+Business Media. http://doi.org/10.1007/978-0-387-98138-3

Eggen, T.J.H.M. (2012) Computerized Adaptive Testing Item Selection in Computerized Adaptive Learning Systems. In: Eggen. TJHM \& Veldkamp, BP.. (Eds). Psychometrics in Practice at RCEC. Enschede: RCEC

Netcoh, S. (2016, March). \“What Do You Mean by \‘Personalized Learning?\’. Croscutting Conversations in Education \– Research, Reflections \& Practice. Blogpost.

Plajner, M., \& Vomlel, J. (2016). Student Skill Models in Adaptive Testing. In Proceedings of the Eighth International Conference on Probabilistic Graphical Models (pp. 403-414).

Van der Linden, W. J., \& Glas, C. A. (2000). Computerized adaptive testing: Theory and practice. Dordrecht: Kluwer Academic Publishers.

Session Video

}, keywords = {feedback selection, item selection, network approach, personalized learning}, author = {Nikky van Buuren and Hendrik Straat and Theo Eggen and Jean-Paul Fox} } @conference {2106, title = {Item Selection Methods based on Multiple Objective Approaches for Classification of Respondents into Multiple Levels}, booktitle = {Annual Conference of the International Association for Computerized Adaptive Testing}, year = {2011}, month = {10/2011}, abstract = {

Is it possible to develop new item selection methods which take advantage of the fact that we want to classify into multiple categories? New methods: Taking multiple points on the ability scale into account; Based on multiple objective approaches.

Conclusions

}, keywords = {adaptive classification test, CAT, item selection, sequential classification test}, author = {Maaike van Groen and Theo Eggen and Bernard Veldkamp} } @article {116, title = {Optimal testing with easy or difficult items in computerized adaptive testing}, journal = {Applied Psychological Measurement}, volume = {30}, number = {5}, year = {2006}, pages = {379-393}, publisher = {Sage Publications: US}, abstract = {Computerized adaptive tests (CATs) are individualized tests that, from a measurement point of view, are optimal for each individual, possibly under some practical conditions. In the present study, it is shown that maximum information item selection in CATs using an item bank that is calibrated with the one- or the two-parameter logistic model results in each individual answering about 50\% of the items correctly. Two item selection procedures giving easier (or more difficult) tests for students are presented and evaluated. Item selection on probability points of items yields good results only with the one-parameter logistic model and not with the two-parameter logistic model. An alternative selection procedure, based on maximum information at a shifted ability level, gives satisfactory results with both models. (PsycINFO Database Record (c) 2007 APA, all rights reserved)}, keywords = {computer adaptive tests, individualized tests, Item Response Theory, item selection, Measurement}, isbn = {0146-6216 (Print)}, author = {Theo Eggen and Verschoor, Angela J.} }