| 1. |
- Kanebrant, Erik, et al.
(author)
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T-MASTER -- A tool for assessing students' reading abilities
- 2015
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In: <em>Proceedings of the 7th International Conference on Computer Supported Education (CSEDU 2015),</em> Lisbon, Portugal. - : SciTePress. - 9789897581083 - 9789897581076 ; , s. 220-227
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Conference paper (peer-reviewed)abstract
- We present T-MASTER, a tool for assessing students’ reading skills on a variety of dimensions. T-MASTER uses sophisticated measures for assessing a student’s reading comprehension and vocabulary understanding. Texts are selected based on their difficulty using novel readability measures and tests are created based on the texts. The results are analyzed in T-MASTER, and the numerical results are mapped to textual descriptions that describe the student’s reading abilities on the dimensions being analysed. These results are presented to the teacher in a form that is easily comprehensible, and lends itself to inspection of each individual student’s results.
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| 2. |
- Jaber, Mohammad, et al.
(author)
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Analysing Online Education-based Asynchronous Communication Tools to Detect Students' Roles
- 2015
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In: CSEDU 2015 - Proceedings of the 7th International Conference on Computer Supported Education. - : SciTePress. - 9789897581083 ; , s. 416-424
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Conference paper (peer-reviewed)abstract
- This paper studies the application of Educational Data Mining to examine the online communication behaviour of students working together on the same project in order to identify the different roles played by the students. Analysis was carried out using real data from students' participation in project communication tools. Several sets of features including individual attributes and information about the interactions between the project members were used to train different classification algorithms. The results show that considering the individual attributes of students provided regular classification performance. The inclusion of information about the reply relationships among the project members generally improved mapping students to their roles. However, it was necessary to add ``time-based'' features in order to achieve the best classification results, which showed both precision and recall of over 95\% for a number of algorithms. Most of these ``time-based'' features coincided with the first weeks of the experience, which indicates the importance of initial interactions between project members.
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