Measuring the Quality of Test-based Exercises Based on the Performance of Students
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Measuring the Quality of Test-based Exercises Based on the Performance of Students Josu Arruarte 1 & Mikel Larrañaga 1 & Ana Arruarte 1 & Jon A. Elorriaga 1 Received: 4 December 2019 / Revised: 13 July 2020 / Accepted: 21 July 2020 # International Artificial Intelligence in Education Society 2020
Abstract In order to be effective, a learning process requires the use of valid and suitable educational resources. However, measuring the quality of an educational resource is not an easy task for a teacher. The data of the performance of the students can be used to measure how appropriate the didactic resources are. Besides this data, adequate metrics and statistics are also needed. In this paper, TEA, a Visual Learning Analytics tool for measuring the quality of a particular type of educational resources, in particular test-based exercises, is presented. TEA is a teacheroriented tool aimed at helping them to improve the quality of the learning material they have created by analyzing and visualizing the performance of the students. TEA evaluates not only the adequacy of individual items but also the appropriateness of a whole test. TEA provides the results of the evaluation so that they are easily interpretable by teachers and developers of educational material. The development of TEA required a thorough analysis and classification of metrics and statistics to identify those which are useful to measure the quality of testbased exercises using the data about the performance of the students. The tool provides visual representations of the performance of the students to allow teachers to evaluate the appropriateness of the test-based exercises they have created. The experimentation carried out with TEA at higher education level is also presented. Keywords Test-based exercises . Visual Learning Analytics . Quality evaluation of test-
based exercises
Introduction Advances in the psychology of learning, especially in our understanding of higher-order cognitive processes, have led to a steady evolution in understanding
* Jon A. Elorriaga [email protected] Extended author information available on the last page of the article
International Journal of Artificial Intelligence in Education
the teaching-learning process. Instead of viewing the learner as an agent reacting to the stimuli generated by the teacher, the learner is considered to be an active participant in the teaching-learning process (Weinstein and Mayer 1986). Professor Jim Greer is one of the pioneers in the application of Artificial Intelligence in education to construct teaching-learning systems adapted to the needs of the students. From this learner-centred perspective, in a joint effort, the GaLan research group of the University of the Basque Country UPV/EHU and the ARIES laboratory of the University of Saskatchewan developed a cognitive theory of instruction, the CLAI (Cognitive Learning from Automated Instruction) Model, under the supervision of Professor Isabel Fernández de Castro and Professor Jim Greer (Arruarte et al. 1996). Professor Jim Greer’s
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