What makes a great MOOC? An interdisciplinary analysis of student retention in online courses

Massive Open Online Courses (MOOCs) have experienced rapid expansion and gained significant popularity among students and educators. Although the broad acceptance of MOOCs, there is still a long way to go in terms of satisfaction of students\u27 needs, as witnessed in the extremely high drop-out rates. Working toward improving MOOCs, we employ the Grounded Theory Method (GTM) in a quantitative study and explore this new phenomenon. In particular, we present a novel analysis using a real-world data set with user-generated online reviews, where we both identify the student, course, platform, and university characteristics that affect student retention and estimate their relative effect. In the conducted analysis, we integrate econometric, text mining, opinion mining, and machine learning techniques, building both explanatory and predictive models, toward a more complete analysis. This study also provides actionable insights for MOOCs and education, in general, and contributes to the related literature discovering new findings

A Review of Data Mining in Personalized Education: Current Trends and Future Prospects

Personalized education, tailored to individual student needs, leverages educational technology and artificial intelligence (AI) in the digital age to enhance learning effectiveness. The integration of AI in educational platforms provides insights into academic performance, learning preferences, and behaviors, optimizing the personal learning process. Driven by data mining techniques, it not only benefits students but also provides educators and institutions with tools to craft customized learning experiences. To offer a comprehensive review of recent advancements in personalized educational data mining, this paper focuses on four primary scenarios: educational recommendation, cognitive diagnosis, knowledge tracing, and learning analysis. This paper presents a structured taxonomy for each area, compiles commonly used datasets, and identifies future research directions, emphasizing the role of data mining in enhancing personalized education and paving the way for future exploration and innovation.Comment: 25 pages, 5 figure

Student Engagement in Aviation Moocs: Identifying Subgroups and Their Differences

The purpose of this study was to expand the current understanding of learner engagement in aviation-related Massive Open Online Courses (MOOCs) through cluster analysis. MOOCs, regarded for their low- or no-cost educational content, often attract thousands of students who are free to engage with the provided content to the extent of their choosing. As online training for pilots, flight attendants, mechanics, and small unmanned aerial system operators continues to expand, understanding how learners engage in optional aviation-focused, online course material may help inform course design and instruction in the aviation industry. In this study, Moore’s theory of transactional distance, which posits psychological or communicative distance can impede learning and success, was used as a descriptive framework for analysis. Archived learning analytics datasets from two 2018 iterations of the same small unmanned aerial systems MOOC were cluster-analyzed (N = 1,032 and N = 4,037). The enrolled students included individuals worldwide; some were affiliated with the host institution, but most were not. The data sets were cluster analyzed separately to categorize participants into common subpopulations based on discussion post pages viewed and posts written, video pages viewed, and quiz grades. Subgroup differences were examined in days of activity and record of completion. Pre- and postcourse survey data provided additional variables for analysis of subgroup differences in demographics (age, geographic location, education level, employment in the aviation industry) and learning goals. Analysis of engagement variables revealed three significantly different subgroups for each MOOC. Engagement patterns were similar between MOOCs for the most and least engaged groups, but differences were noted in the middle groups; MOOC 1’s middle group had a broader interest in optional content (both in discussions and videos); whereas MOOC 2’s middle group had a narrower interest in optional discussions. Mandatory items (Mandatory Discussion or Quizzes) were the best predictors in classifying subgroups for both MOOCs. Significant associations were found between subgroups and education levels, days of activity, and total quiz scores. This study addressed two known problems: a lack of information on student engagement in aviation-related MOOCs, and more broadly, a growing imperative to examine learners who utilize MOOCs but do not complete them. This study served as an important first step for course developers and instructors who aim to meet the diverse needs of the aviation-education community

Analysis of Student Behavior and Score Prediction in Assistments Online Learning

Understanding and analyzing student behavior is paramount in enhancing online learning, and this thesis delves into the subject by presenting an in-depth analysis of student behavior and score prediction in the ASSISTments online learning platform. We used data from the EDM Cup 2023 Kaggle Competition to answer four key questions. First, we explored how students seeking hints and explanations affect their performance in assignments, shedding light on the role of guidance in learning. Second, we looked at the connection between students mastering specific skills and their performance in related assignments, giving insights into the effectiveness of curriculum alignment. Third, we identified important features from student activity data to improve grade prediction, helping identify at-risk students early and monitor their progress. Lastly, we used graph representation learning to understand complex relationships in the data, leading to more accurate predictive models. This research enhances our understanding of data mining in online learning, with implications for personalized learning and support mechanisms

Explaining trace-based learner profiles with self-reports:The added value of psychological networks

Background: Learner profiles detected from digital trace data are typically triangulated with survey data to explain those profiles based on learners' internal conditions (e.g., motivation). However, survey data are often analysed with limited consideration of the interconnected nature of learners' internal conditions. Objectives: Aiming to enable a thorough understanding of trace-based learner profiles, this paper presents and evaluates a comprehensive approach to analysis of learners' self-reports, which extends conventional statistical methods with psychological networks analysis. Methods: The study context is a massive open online course (MOOC) aimed at promoting physical activity (PA) for health. Learners' (N = 497) perceptions related to PA, as well as their self-efficacy and intentions to increase the level of PA were collected before and after the MOOC, while their interactions with the course were logged as digital traces. Learner profiles derived from trace data were further examined and interpreted through a combined use of conventional statistical methods and psychological networks analysis. Results and Conclusions: The inclusion of psychological networks in the analysis of learners' self-reports collected before the start of the MOOC offers better understanding of trace-based learner profiles, compared to the conventional statistical analysis only. Likewise, the combined use of conventional statistical methods and psychological networks in the analysis of learners' self-reports before and after the MOOC provided more comprehensive insights about changes in the constructs measured in each learner profile. Major Takeaways: The combined use of conventional statistical methods and psychological networks presented in this paper sets a path for a comprehensive analysis of survey data. The insights it offers complement the information about learner profiles derived from trace data, thus allowing for a more thorough understanding of learners' course engagement than any individual method or data source would allow.</p

Gamification: a key determinant of massive open online course (MOOC) success

Massive open online courses (MOOCs), contribute significantly to individual empowerment because they can help people learn about a wide range of topics. To realize the full potential of MOOCs, we need to understand their factors of success, here defined as the use, user satisfaction, along the individual and organizational performance resulting from the user involvement. We propose a theoretical framework to identify the determinants of successful MOOCs, and empirically measure these factors in a real MOOC context. We put forward the role of gamification and suggest that, together with information system (IS) theory, gamification proved to play a crucial role in the success of MOOCs.info:eu-repo/semantics/acceptedVersio

Analyzing the behavior of students regarding learning activities, badges, and academic dishonesty in MOOC environment

Mención Internacional en el título de doctorThe ‘big data’ scene has brought new improvement opportunities to most products and services, including education. Web-based learning has become very widespread over the last decade, which in conjunction with the Massive Open Online Course (MOOC) phenomenon, it has enabled the collection of large and rich data samples regarding the interaction of students with these educational online environments. We have detected different areas in the literature that still need improvement and more research studies. Particularly, in the context of MOOCs and Small Private Online Courses (SPOCs), where we focus our data analysis on the platforms Khan Academy, Open edX and Coursera. More specifically, we are going to work towards learning analytics visualization dashboards, carrying out an evaluation of these visual analytics tools. Additionally, we will delve into the activity and behavior of students with regular and optional activities, badges and their online academically dishonest conduct. The analysis of activity and behavior of students is divided first in exploratory analysis providing descriptive and inferential statistics, like correlations and group comparisons, as well as numerous visualizations that facilitate conveying understandable information. Second, we apply clustering analysis to find different profiles of students for different purposes e.g., to analyze potential adaptation of learning experiences and pedagogical implications. Third, we also provide three machine learning models, two of them to predict learning outcomes (learning gains and certificate accomplishment) and one to classify submissions as illicit or not. We also use these models to discuss about the importance of variables. Finally, we discuss our results in terms of the motivation of students, student profiling, instructional design, potential actuators and the evaluation of visual analytics dashboards providing different recommendations to improve future educational experiments.Las novedades en torno al ‘big data’ han traído nuevas oportunidades de mejorar la mayoría de productos y servicios, incluyendo la educación. El aprendizaje mediante tecnologías web se ha extendido mucho durante la última década, que conjuntamente con el fenómeno de los cursos abiertos masivos en línea (MOOCs), ha permitido que se recojan grandes y ricas muestras de datos sobre la interacción de los estudiantes con estos entornos virtuales de aprendizaje. Nosotros hemos detectado diferentes áreas en la literatura que aún necesitan de mejoras y del desarrollo de más estudios, específicamente en el contexto de MOOCs y cursos privados pequeños en línea (SPOCs). En la tesis nos hemos enfocado en el análisis de datos en las plataformas Khan Academy, Open edX y Coursera. Más específicamente, vamos a trabajar en interfaces de visualizaciones de analítica de aprendizaje, llevando a cabo la evaluación de estas herramientas de analítica visual. Además, profundizaremos en la actividad y el comportamiento de los estudiantes con actividades comunes y opcionales, medallas y sus conductas en torno a la deshonestidad académica. Este análisis de actividad y comportamiento comienza primero con análisis exploratorio proporcionando variables descriptivas y de inferencia estadística, como correlaciones y comparaciones entre grupos, así como numerosas visualizaciones que facilitan la transmisión de información inteligible. En segundo lugar aplicaremos técnicas de agrupamiento para encontrar distintos perfiles de estudiantes con diferentes propósitos, como por ejemplo para analizar posibles adaptaciones de experiencias educativas y sus implicaciones pedagógicas. También proporcionamos tres modelos de aprendizaje máquina, dos de ellos que predicen resultados finales de aprendizaje (ganancias de aprendizaje y la consecución de certificados de terminación) y uno para clasificar que ejercicios han sido entregados de forma deshonesta. También usaremos estos tres modelos para analizar la importancia de las variables. Finalmente, discutimos todos los resultados en términos de la motivación de los estudiantes, diferentes perfiles de estudiante, diseño instruccional, posibles sistemas actuadores, así como la evaluación de interfaces de analítica visual, proporcionando recomendaciones que pueden ayudar a mejorar futuras experiencias educacionales.Programa Oficial de Doctorado en Ingeniería TelemáticaPresidente: Davinia Hernández Leo.- Secretario: Luis Sánchez Fernández.- Vocal: Adolfo Ruiz Callej