Search Results (13)

Personalized learning environments increasingly rely on learner modeling techniques that integrate both explicit and implicit data sources. This study introduces a hybrid profiling methodology that combines psychometric data from an extended Felder–Silverman Learning Style Model (FSLSM) questionnaire with behavioral analytics derived from Moodle Learning Management System interaction logs. A structured mapping process was employed to associate over 200 unique log event types with FSLSM cognitive dimensions, enabling dynamic, behavior-driven learner profiles. Experiments were conducted across three datasets: a university dataset from the International Hellenic University, a public dataset from Kaggle, and a combined dataset totaling over 7 million log entries. Deep learning models including a Sequential Neural Network, BiLSTM, and a pretrained MLSTM-FCN were trained to predict student performance across regression and classification tasks. Results indicate moderate predictive validity: binary classification achieved practical, albeit imperfect accuracy, while three-class and regression tasks performed close to baseline levels. These findings highlight both the potential and the current constraints of log-based learner modeling. The contribution of this work lies in providing a reproducible integration framework and pipeline that can be applied across datasets, offering a realistic foundation for further exploration of scalable, data-driven personalization. Full article

The rapid growth of online education has raised significant concerns about identifying and addressing academic dishonesty in online exams. Although existing solutions aim to prevent and detect such misconduct, they often face limitations that make them impractical for many educational institutions. This paper introduces a novel online education integrity system utilizing well-established statistical methods to identify academic dishonesty. The system has been developed and integrated as an open-source Moodle plug-in. The evaluation involved utilizing an open-source Moodle quiz log database and creating synthetic benchmarks that represented diverse forms of academic dishonesty. The findings indicate that the system accurately identifies instances of academic dishonesty. The anticipated deployment includes institutions that rely on the Moodle Learning Management System (LMS) as their primary platform for administering online exams. Full article

With the wide adoption of Learning Management Systems (LMSs) in educational institutions, ample data have become available demonstrating students’ online behavior. Digital traces are widely applicable in Learning Analytics (LA). This study aims to explore and extract behavioral features from Moodle logs and examine their effect on undergraduate students’ performance. Additionally, traditional factors such as demographics, academic history, family background, and attendance data were examined, highlighting the prominent features that affect student performance. From January to April 2019, a total of 64,231 students’ Moodle logs were collected from a private university in Malaysia for analyzing students’ behavior. Exploratory Data Analysis, correlation, statistical tests, and post hoc analysis were conducted. This study reveals that age is found to be inversely correlated with student performance. Tutorial attendance and parents’ occupations play a crucial role in students’ performance. Additionally, it was found that online engagement during the weekend and nighttime positively correlates with academic performance, representing a 10% relative increase in the student’s exam score. Ultimately, it was found that course views, forum creation, overall assignment interaction, and time spent on the platform were among the top LMS variables that showed a statistically significant difference between successful and failed students. In the future, clustering analysis can be performed in order to reveal heterogeneous groups of students along with specific course-content-based logs. Full article

The log files of Massive Open Online Courses (MOOCs) reveal useful information that can help interpret student behavior. In this study, we focus on student performance based on their access to course resources and the grades they achieve. We define states as the Moodle resources and quiz grades for each student ID, considering participation in resources such as wikis and forums. We use efficient Hidden Markov Models to interpret the abundance of information provided in the Moodle log files. The transitions among certain resources for each student or groups of students are determined as behaviors. Other studies employ Machine Learning and Pattern Classification algorithms to recognize these behaviors. As an example, we visualize these transitions for individual learners. Additionally, we have created row and column charts to present our findings in a comprehensible manner. For implementing the proposed methodology, we use the R programming language. The dataset that we use was obtained from Kaggle and pertains to a MOOC of 4037 students. Full article

In distance learning educational environments like Moodle, students interact with their tutors, their peers, and the provided educational material through various means. Due to advancements in learning analytics, students’ transitions within Moodle generate digital trace data that outline learners’ self-directed learning paths and reveal information about their academic behavior within a course. These learning paths can be depicted as sequences of transitions between various states, such as completing quizzes, submitting assignments, downloading files, and participating in forum discussions, among others. Considering that a specific learning path summarizes the students’ trajectory in a course during an academic year, we analyzed data on students’ actions extracted from Moodle logs to investigate how the distribution of user actions within different Moodle resources can impact academic achievements. Our analysis was conducted using a Markov Chain Model, whereby transition matrices were constructed to identify steady states, and eigenvectors were calculated. Correlations were explored between specific states in users’ eigenvectors and their final grades, which were used as a proxy of academic performance. Our findings offer valuable insights into the relationship between student actions, link weight vectors, and academic performance, in an attempt to optimize students’ learning paths, tutors’ guidance, and course structures in the Moodle environment. Full article

Moodle is a platform designed for universal learning to support pedagogical interactions and educational activities. The information technology (IT) administrator uses standard authentication methods for students logging into the Moodle platform. The need for two-factor authentication has grown as institutions, governments, and individuals realize that passwords are not secure enough to protect user accounts in their current technical format. The classic connection methods have vulnerabilities, and account passwords are easy to crack. Analyzing these aspects, the goal is to create a new safe and reliable alternative to the traditional authentication methods in e-learning platforms. The proposed solution introduces a new authentication factor using digital certificates stored on physical devices or the cloud to address the evolving authentication and security challenges effectively. The absence of this authentication within the Moodle ecosystem has imparted a sense of urgency for its implementation. With the innovative authentication scheme, the users have gained confidence, are satisfied with the new solution, and have not reported security breaches. The result is increased security, data protection, and better account management. Full article

Educational data mining is a process that aims at discovering patterns that provide insight into teaching and learning processes. This work uses Machine Learning techniques to create a student performance prediction model, using academic data and records from a Learning Management System, that correlates with success or failure in completing the course. Six algorithms were employed, with models trained at three different stages of their two-year course completion. We tested the models with records of 394 students from 3 courses. Random Forest provided the best results with 84.47% on the F1 score in our experiments, followed by Decision Tree obtaining similar results in the first subjects. We also employ clustering techniques and find different behavior groups with a strong correlation to performance. This work contributes to predicting students at risk of dropping out, offers insight into understanding student behavior, and provides a support mechanism for academic managers to take corrective and preventive actions on this problem. Full article

The COVID-19 pandemic and lockdown brought numerous teaching challenges requiring innovative approaches to teaching and learning, including novel modes of content delivery, virtual classrooms, and online assessment schemes. The aim of this study is to describe and assess the efficacy of the methods implemented at the University of León (Spain) to adapt to lockdowns in the context of the Cytology and Histology (CH) course for veterinary medicine undergraduate students. To evaluate the success of lockdown-adapted methodologies, we used inferential statistical analysis to compare the academic outcomes of two cohorts: 2018–2019 (traditional face-to-face—presential—learning and evaluation) and 2019–2020 (some face-to-face and some online lockdown-adapted learning and online lockdown-adapted evaluation). This analysis considered scores in both theoretical and practical exams and students’ final subject score. We also evaluated the number of logs onto the Moodle platform throughout the 2019–2020 period, as well as performing a student satisfaction survey in both courses. The use of explanatory pre-recorded lectures, continuous online self-assessment tests, and virtual microscopy (VM) may have produced significant improvements in the acquisition of histology competencies among students in the lockdown cohort. However, we need to implement further strategies to improve the assessment of students’ true level of knowledge acquisition. According to the student feedback, VM is a well-accepted resource that is perceived as a flexible and enjoyable tool to use. However, while students found that the resource enhances their ability to learn about microscopic structures, they felt that it should not completely replace optical microscopy. Full article

Educational institutions are transferring analytics computing to the cloud to reduce costs. Any data transfer and storage outside institutions involve serious privacy concerns, such as student identity exposure, rising untrusted and unnecessary third-party actors, data misuse, and data leakage. Institutions that adopt a “local first” approach instead of a “cloud computing first” approach can minimize these problems. The work aims to foster the use of local analytics computing by offering adequate nonexistent tools. Results are useful for any educational role, even investigators, to conduct data analysis locally. The novelty results are twofold: an open-source JavaScript library to analyze locally any educational log schema from any LMS; a front-end to analyze Moodle logs as proof of work of the library with different educational metrics and indicator visualizations. Nielsen heuristics user experience is executed to reduce possible users’ data literacy barrier. Visualizations are validated by surveying teachers with Likert and open-ended questions, which consider them to be of interest, but more different data sources can be added to improve indicators. The work reinforces that local educational data analysis is feasible, opens up new ways of analyzing data without data transfer to third parties while generating debate around the “local technologies first” approach adoption. Full article

In this study, we used a module for monitoring and detecting students at risk of dropping out. We worked with a sample of 49 third-year students in a Health Science degree during a lockdown caused by COVID-19. Three follow-ups were carried out over a semester: an initial one, an intermediate one and a final one with the UBUMonitor tool. This tool is a desktop application executed on the client, implemented with Java, and with a graphic interface developed in JavaFX. The application connects to the selected Moodle server, through the web services and the REST API provided by the server. UBUMonitor includes, among others, modules for log visualisation, risk of dropping out, and clustering. The visualisation techniques of boxplots and heat maps and the cluster analysis module (k-means ++, fuzzy k-means and Density-based spatial clustering of applications with noise (DBSCAN) were used to monitor the students. A teaching methodology based on project-based learning (PBL), self-regulated learning (SRL) and continuous assessment was also used. The results indicate that the use of this methodology together with early detection and personalised intervention in the initial follow-up of students achieved a drop-out rate of less than 7% and an overall level of student satisfaction with the teaching and learning process of 4.56 out of 5. Full article

Teaching in Higher Education is with increasing frequency completed within a Learning Management System (LMS) environment in the Blended Learning modality. The use of learning objects (activities and resources) offered by LMS means that both teachers and students require training. In addition, gender differences relating to the number of students in STEM (Science, Technology, Engineering, and Mathematics) and Non-STEM courses might have some influence on the use of those learning objects. The study involves 13 teachers (6 experts in e-Learning and 7 non-experts) on 13 academic courses (4 STEM and 9 Non-STEM) and a detailed examination of the logs of 626 students downloaded from the Moodle platform. Our objectives are: (1) To confirm whether significant differences may be found in relation to the use of learning objects (resources and activities) on Moodle, depending on the expertise of the teacher (expert vs. non-expert in e-Learning); (2) To confirm whether there are significant differences between students regarding their use of learning objects, depending on the expertise of the teacher (expert vs. non-expert in e-Learning); (3) To confirm whether there are significant differences for the use of learning objects among students as a function of gender. Differences were found in the use of Moodle learning objects (resources and activities) for teachers and for students depending on the expertise of the teacher. Likewise, differences were found for the use of some learning objects as a function of gender and the degrees that the students were following. Increased technological training for both teachers and students is proposed, especially on Non-STEM qualifications, in order to mitigate the effects of the technological gap and its collateral relation with the gender gap and the digital divide. Full article

Predicting students’ academic performance is one of the older challenges faced by the educational scientific community. However, most of the research carried out in this area has focused on obtaining the best accuracy models for their specific single courses and only a few works have tried to discover under which circumstances a prediction model built on a source course can be used in other different but similar courses. Our motivation in this work is to study the portability of models obtained directly from Moodle logs of 24 university courses. The proposed method intends to check if grouping similar courses by the degree or the similar level of usage of activities provided by the Moodle logs, and if the use of numerical or categorical attributes affect in the portability of the prediction models. We have carried out two experiments by executing the well-known classification algorithm over all the datasets of the courses in order to obtain decision tree models and to test their portability to the other courses by comparing the obtained accuracy and loss of accuracy evaluation measures. The results obtained show that it is only feasible to directly transfer predictive models or apply them to different courses with an acceptable accuracy and without losing portability under some circumstances. Full article

The way of teaching and learning in twenty-first century society continues to change. At present, a high percentage of teaching takes place through Learning Management Systems that apply Learning Analytics Techniques. The use of these tools, among other things, facilitates knowledge of student learning patterns and the detection of at-risk students. The aim of this study is to establish the most effective learning patterns of the students on the platform in a hierarchical order of importance. It was conducted over two academic years with 122 students of Health Sciences. The instruments used were the Moodle v.3.1 platform and the analysis of logs with Machine Learning regression techniques. The results indicated that the Automatic Linear Prediction Model detected by order of importance: average visits per day, student self-assessment questionnaires, and teacher feedback. The percentage variance of the final results explained by these variables was 50.8%. Likewise, the effectiveness of the behavioral pattern explained 64.1% of the variance in those results, finding three clusters of effectiveness in the behavioral patterns that were detected. Full article