Personalized Learning Model Using Item Response Theory
Amy Lyn M. Maddalora

Amy Lyn M. Maddalora, DIT, Isabela State University, Cabagan.
Manuscript received on 06 June 2019 | Revised Manuscript received on 30 June 2019 | Manuscript Published on 04 July 2019 | PP: 811-818 | Volume-8 Issue-1S4 June 2019 | Retrieval Number: A11500681S419/2019©BEIESP
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Abstract: Many Higher Education Institutions in the Philippines now accept that a blended approach offers countless advantages in most areas of learning. However, learner’s ability has been neglected as a significant factor in student’s success. Thus various techniques have been developed such as personalization to improve the learning process and accommodate diversity of learners. This study introduces a personalized learning model that recommends Shortest Learning Sequence (SLS) to remediate students with learning difficulty. The learning model was created using Item Response Theory (IRT) implemented in an e-learning environment. Assessments were given during the learning process with levels of difficulty. IRT probabilistically estimates the student’s proficiency of the topics taking into considerations the difficulty of the test items. SLS consists of lessons recommended to students, which were ranked accordingly. The one parameter (1PL) model was used to evaluate the test score and the item information was used to rank the lessons. Lessons are reduced until the proficiency level of the student is reached. Results show that the personalized learning model is capable of recommending shortest learning sequences. Hence, reduces the time spent of study. The student’s proficiency level was increased through the implementation of the personalized learning model. Thus, the learning outcome was also improved.
Keywords: Shortest Learning Sequence; Item Response Theory; E-Learning; Personalized Learning Model.
Scope of the Article: Digital Signal Processing Theory