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…

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…

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…

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…

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…

Interactive learning environments can generate massive learning behavior data and the support of learning behavior big data can ensure the completeness of data analysis and robustness of relationship verification. In this study, learning behaviors are divided into training set and testing set, BP neural network and recurrent Elman network are…

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…

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…

Visual analytics (VA) technology has become a significant tool for business decision-makers to explore novel insights. One of its key features is analytical reasoning through interactive visualization that users have direct sensemaking in problem-solving processes. Few studies have examined how VA can be applied in business education for improving…

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…

The paper delves into social media mining in the context of medical education programs in the information age. It explores the adaptability of Social Media Analytics (SMA) apps within the structure of online courses in medicine and proposes a conceptual framework for a learning process. This process includes practical exercises based on search and…

The proliferation of massive open online courses (MOOCs) highlights the necessity of developing accurate and diagnostic evaluation methods to assess the courses' quality and effectiveness. Hence, this study proposes a diagnostic MOOC evaluation (DME) method that combines the Analytic Hierarchy Process algorithm and learner review mining to…

Attrition is one of the main concerns in distance learning due to the impact on the incomes and institutions reputation. Timely identification of students at risk has high practical value in effective students' retention services. Big Data mining and machine learning methods are applied to manipulate, analyze and predict students' failure,…

Attrition is one of the main concerns in distance learning due to the impact on the incomes and institutions reputation. Timely identification of students at risk has high practical value in effective students' retention services. Big Data mining and machine learning methods are applied to manipulate, analyze, and predict students' failure,…

Performance Factors Analysis (PFA) is considered one of the most important Knowledge Tracing (KT) approaches used for constructing adaptive educational hypermedia systems. It has shown a high prediction accuracy against many other KT approaches. While, the desire to estimate more accurately the student level leads researchers to enhance PFA by…