K-Tips: Knowledge Extension Based on Tailor-Made Information Provision System

Abstract

Thanks to an increase in the amount of information on the Internet and the spread of ICT-supported educational environments, much attention has been paid to learning support based on "smart" recommendation technologies. In this study, we propose an education improvement model based on the recommender system using the human-in-the-loop design strategy. Our proposed model enhances not only learners via recommendation, but also teachers and the system itself through the interaction between teachers and the system. In this paper, we introduce the details of the proposed model and implementation strategy followed by a report of preliminary experimental results.

Adaptive Learning Support System Based on Automatic Recommendation of Personalized Review Materials

“…Thaker et al [14] proposed a system that updates the students' state of knowledge based on the results of quizzes to adaptively recommend text materials. Furthermore, there are several systems that recommend related web pages [36], [37], [38]. Beel et al [36] proposed a method to recommend articles in Wikipedia related to each chapter of open-source online textbooks.…”

“…Beel et al [36] proposed a method to recommend articles in Wikipedia related to each chapter of open-source online textbooks. Nakayama et al [37], [38] proposed a method to extract important words from digital textbooks using TF-IDF and recommend web pages related to each page of digital textbooks. Chris et al [39] proposed a method called deep knowledge tracing (DKT), which models the learner's knowledge state by applying a recurrent neural network (RNN) [40] to the answer history of exercises.…”

“…Numerous studies have focused on creating hyperlinks to external webpages for entire textbooks or for each chapter of a textbook, such as the study on automatically creating hyperlinks between textbooks and Wikipedia [36]. Nakayama et al proposed a method to recommend web pages related to the contents of each page of a digital textbook [38], [37]. Because our system also targets the same digital learning materials, we used this method to recommend web pages related to the contents of each page.…”

Personalized Smart Learning Recommendation System for Arabic Users in Smart Campus

“…First, the topics with high user preference score were estimated and recommended using LDA technique. To help students understand course content, Nakayama et al (2019) developed an e-learning recommendation system. They extracted keywords from each page of an electronic manual to retrieve the related websites.…”

Recurrent Neural Network-FitNets: Improving Early Prediction of Student Performanceby Time-Series Knowledge Distillation

“…In recent decades, research fields analyzing such educational big data to understand and optimize learning and the environment have rapidly grown (Liñán & Pérez, 2015). Various research topics include learning behavior analysis (Wang et al, 2016; Mouri et al, 2019), personalized learning material recommender systems (Wan & Niu., 2018; Nakayama et al, 2019), and real-time analytics during lectures (Diana et al, 2017; Owatari et al, 2020).…”