The aim of this paper is to categorize and describe different types of learners in massive open online courses (MOOCs) by means of a subgroup discovery (SD) approach based on MapReduce. The proposed SD approach, which is an extension of the well-known FP-Growth algorithm, considers emerging parallel methodologies like MapReduce to be able to cope…

In this study, we propose a MOOC Analytic Statistical Visual model (MOOC-ASV) to explore students' engagement in MOOC courses and predict their performance on the basis of their behaviors logged as big data in MOOC platforms. The model has multifunctions, which performs on visually analyzing learners' data by state-of-the-art techniques. The model…

This paper studies the problem of coordination and supervision of virtual teams and their capabilities. The goal is to develop a model suitable for managing virtual student teams specialized in the development of smart environments. The developed model is based on SAFe and DevOps, which when combined provide us with a framework for the evaluation…

Predictive models on students' academic performance can be built by using historical data for modelling students' learning behaviour. Such models can be employed in educational settings to determine how new students will perform and in predicting whether these students should be classed as at-risk of failing a course. Stakeholders can use…

Datasets are critical for emotion analysis in the machine learning field. This study aims to explore emotion analysis datasets and related benchmarks in online learning, since, currently, there are very few studies that explore the same. We have scientifically labeled the topic and nine-category emotion of 4715 comment texts in online learning…

The purpose of this study is to review journal papers on educational big data research published from 2010 to 2018. A total of 143 papers were selected. The papers were characterized based on three dimensions: (a) educational goals; (b) educational problems addressed; and (c) big data analytical techniques used. A qualitative content analysis…

This paper describes a method to collect data of which section of pages learners were browsing in digital textbooks without eye-tracking technologies. In previous researches on digital textbook systems, it was difficult to collect such data without using eye-tackers. However, eye-trackers cost a massive budget. Our proposed system automatically…

Gamification has gained an increasing attention from researchers and practitioners in various domains including education as it can increase learners' engagement and motivation. However, little is known about how educational gamification experiences can be influenced by learners' characteristics. Therefore, this study provides a systematic…

To examine the similarities and differences between two closely related yet distinct fields -- Educational Data Mining (EDM) and Learning Analytics (LA) -- this study conducted a literature review of the empirical studies published in both fields. We synthesized 492 LA and 194 EDM articles published during 2015-2019. We compared the similarities…

Emerging technologies such as augmented reality are impacting the field of education, it can be seen in the attention given to technology, specifically augmented reality. Systematic reviews on research related to learning through augmented reality aim to find gaps of augmented reality in learning. However, there is a limited number of systematic…

Today's dynamic distance learning environments offer a flexible, comfortable, and lifelong learning experience, independent of space and time. In this way, it also supports and develops existing traditional training programs. The increasing importance of knowledge, skills and learning in today's technological life cycle has led to an increase and…

This interventional case study adopted a data-supported reflective assessment (DSRA) design to help pre-service teachers (PTs) engage in effective Knowledge Building (KB) and examined the mechanisms of this design to support PTs' productive KB discourse. The participants were 80 PTs from two classes taking the same course. Statistical analysis of…

High dropout rate exists universally in massive open online courses (MOOCs) due to the separation of teachers and learners in space and time. Dropout prediction using the machine learning method is an extremely important prerequisite to identify potential at-risk learners to improve learning. It has attracted much attention and there have emerged…

Learning interaction activities are the key part of tracking and evaluating learning behaviors, that plays an important role in data-driven autonomous learning and optimized learning in interactive learning environments. In this study, a big data set of learning behaviors with multiple learning periods is selected. According to the instance…

The main aim of this research was to improve a blended learning course, by adding specific online activity that will improve learning outcomes and enable producing, collecting and analysing educational data. Moodle LMS, a widely used, well-known learning environment, was used for realisation of the online activity. Data collected over LMS Moodle…