Remedial Directed Topic Map on Personalized Scaffolding Adaptive Learning Management System

EasyChair Preprint 7345

Abstract

The problem arises behind the mastery learning model, that is some students fail to achieve the minimum standard of completeness. The existing solutions have been ineffective in helping the study plan. The existing remedial method does not consider the previous formative test answers, but all material that has been mastered must also be tested. This remedial method did not give students more time to learn misconceptions or the material they had not mastered yet. We propose a framework for building a scaffold on a remedial learning path that is personal and adaptive to the material needs that students must learn. Initial exploration in this study aims to look the possibility in implement the proposed framework by utilizing machine learning on mastery module using neural network and knowledge-based recommendation technique to process the input of student’s answers, domain expert's topic maps, and the relationship among answers to build a computer-based scaffold. This novelty of exercise learning path could be an effective remedial path for the failed student to learn just according to topics have not mastered.

Keyphrases: Topic Map, adaptive scaffolding, assessment-based learning, concept map, machine learning, mastery learning, scaffolding, self-regulated

@booklet{EasyChair:7345,
  author    = {Yulia Wahyuningsih and Arif Djunaidy and Daniel Oranova Siahaan},
  title     = {Remedial Directed Topic Map on Personalized Scaffolding Adaptive Learning Management System},
  howpublished = {EasyChair Preprint 7345},
  year      = {EasyChair, 2022}}