ALLS Papers Accepted as Journal Papers in PCJ

Two ALLS papers were accepted as journal papers in the Philippine Computing Journal..

The first paper, entitled “Educational data mining: Current research and open questions”, was written by Dr. Didith Rodrigo.

Educational data mining (EDM) refers to the application of statistical and machine learning methods to educational data in order to achieve one of four typical ends: improvement of student models, improvement of subject matter domain structures, studying pedagogical support and refining educational theories. An interdisciplinary field, EDM draws on mathematics, computer sciences, cognitive psychology, education theory, sociology and others. This paper walks the reader through the EDM process and then discusses recent work and open questions in the first three application areas. The paper hopes to introduce young researchers to the field and suggest problems that are still open for investigation.

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The second paper, entitled “Exploring the implications of tutor negativity towards a synthetic agent in a learning-by-teaching environment”, was written by Dr. Didith Rodrigo, Regina Ira Antonette Geli, Aaron Ong, Gabriel Jose Vitug, Rex Bringula, Roselle Basa, Cecilio dela Cruz, and Noboru Matsuda.

The researchers examine the implications of negativity in free-form dialogue between student tutors and a synthetic agent in APLUS, a learning-by-teaching online learning environment for Algebra. They attempt to determine whether the negativity of a student tutor’s discourse with the agent indicates that the student is learning more or less of the material and whether the feedback they give the synthetic agent is more or less accurate. They found a weak negative correlation between tutor negativity and learning gains and a strong negative correlation between tutor negativity and accuracy of feedback. Negativity might indeed indicate that student tutors lack mastery of the subject matter and need assistance themselves and detecting negativity during tutoring and providing appropriate assistance might enhance the effectiveness of APLUS and other intelligent tutoring systems.

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