Title:	THE FEASIBILITY OF AUTOMATIC ASSESSMENT AND FEEDBACK
Author(s):	Mikko-jussi Laakso , Tapio Salakoski , Ari Korhonen
ISBN:	972-8924-05-4
Editors:	Kinshuk, Demetrios Sampson and Pedro Isaías
Year:	2005
Edition:	Single
Keywords:	Automatic assessment, feedback, computer science education, evaluation.
Type:	Full Paper
First Page:	113
Last Page:	122
Language:	English
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Full Contents:	click to dowload
Paper Abstract:	In this study, we report on the results of studies in which two randomly selected groups of students were monitored while they solved exercises in a Data Structures and Algorithms (DSA) course. The first group did the exercises by using web-based system and the second one in the classroom sessions and the roles of the groups were changed in the middle of the course. A web-based system capable of automatically assessing exercises and giving feedback was employed. The research question was to find out how we should introduce the self study material and automatically assessed exercises to the students in order to maximize their learning experience and to avoid drop outs. In addition, we surveyed the students’ attitude towards web-based exercises by using questionnaires. The students were asked what kind of exercises they would prefer to do in DSA courses as well as how they would assess their own learning experience in the three different setups (human guided, web-based or combination of these two). All these studies were carried out simultaneously in two different universities. The results suggest introducing easy and human guided exercises at the very beginning of the course. However, we conclude that currently there is an emerging need for both web-based and classroom exercises. We claim that the recommended way to introduce the web-based exercises in DSA courses is by combining these two approaches. There is a set of exercises that are the most suitable to be solved and automatically assessed on the web while the rest of the exercises are best suitable for traditional classroom sessions. We believe that the results of this study can be generalized to cover also other similar learning environments than that used in this research to give automated feedback for the students, and thus improve the learning experience.
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