Search Results (127)

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

The development and use of conversational agents in education has become widespread in recent years because of their ability to facilitate student interaction with the learning material, improve engagement and provide academic support, while at the same time reducing the teachers’ workload. This is especially important in the case of distance and asynchronous education, where the availability of academic support must be ensured at any time. This paper reports on the implementation and evaluation of a conversational agent called AIMT Agent, developed for the purposes of supporting postgraduate students during their studies. The conversational agent is based on an open-source framework, namely Rasa, which provides the tools for building natural language understanding into the agent. The agent is fully integrated with the Moodle Learning Management System (LMS). The agent was assessed through a questionnaire according to the Technology Acceptance Model (TAM) in terms of perceived usefulness and perceived ease of use by 24 postgraduate students. The results show that users of the AIMT agent have assessed the conversational agent favorably in both of these aspects. This confirms the validity of the approach and is a motivation for refinements and further development. Full article

The development of higher education worldwide must be integrated in the context of sustainable development in order to ensure environmental sustainability, social sustainability, and economic sustainability. Knowing that the digitalization process within higher education institutions is continuously expanding, while the digital tools and online learning environments enhance accessibility, flexibility, and efficiency, the purpose of the present paper is to highlight how the use of e-Learning platforms can help sustainable education development from the point of view of students from two universities: a university from Romania and a university from the USA. In order to reach this objective, a quantitative research method was used, aiming to identify the students’ opinion on a series of facilities offered by e-Learning platforms that serve as the learning process. Data processing and analysis were conducted by means of specific tests supplied by the SPSS software. The obtained results showed that Moodle users spend approximately 26% more time on the platform than Blackboard users, with a small effect size (d = 0.284), while non-engineering students spend approximately 45% more time on the e-learning platform than engineering students, also with a small effect size (d = 0.458). The evaluation of the efficacy of different educational resources varies based on the e-learning platform used, with a large effect size found for editable files (d = 1.017) and PPT or similar formats (d = 1.2). Also, the efficacy of various applications on the e-learning platform varies by platform type, with a large effect size observed for two-way student–teacher communication (d = 0.819), self-assessment grid tests with immediate feedback (d = 1.072), synchronous online meetings with teachers (d = 1.117), and access to information from previous years (d = 0.89). We conclude that institutions should prioritize platform adaptability and student engagement strategies when implementing e-learning solutions for sustainable development. Full article

The COVID-19 pandemic resulted in the widespread adoption of online instruction all around the world. In fact, in the post-pandemic era, online teaching and learning are proliferating and are considered as alternatives to traditional learning. The current study investigated L2 learners’ perceptions of an online pronunciation course. Sixty L2 learners, ranging in age from 18 to 60, were recruited from different intensive English programs (IEPs) across the United States and six other countries, including India, Brazil, China, France, Russia, and Canada. The participants received online-based computer-assisted pronunciation training (CAPT) on Moodle over a period of three weeks and completed an online survey on Qualtrics. The results of the quantitative and qualitative data collected from the learners at the end of the course showed that the learners were highly satisfied with their own performance and that they found the online course highly useful and preferred it over a face-to-face pronunciation course. The findings provide valuable insights into the design and delivery of online courses for pronunciation teachers. The findings also suggest that CAPT can effectively support asynchronous L2 pronunciation teaching. 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

This study investigates the use of educational data mining (EDM) techniques to predict student performance and enhance learning outcomes in higher education. Leveraging data from Moodle, a widely used learning management system (LMS), we analyzed 450 students’ academic records spanning nine semesters. Five machine learning algorithms—k-nearest neighbors, random forest, logistic regression, decision trees, and neural networks—were applied to identify correlations between courses and predict grades. The results indicated that courses with strong correlations (+0.3 and above) significantly enhanced predictive accuracy, particularly in binary classification tasks. kNN and neural networks emerged as the most robust models, achieving F1 scores exceeding 0.8. These findings underscore the potential of EDM to optimize instructional strategies and support personalized learning pathways. This study offers insights into the effective application of data-driven approaches to improve educational outcomes and foster student success. Full article

Almost two decades have passed since the development of the first (stand-alone) version of the oscilloscope simulator, known as Analogue Oscilloscope Simulator, widely utilized by thousands of Electrical and Computer Engineering (ECE) students at Instituto Superior de Engenharia do Porto (ISEP). Meanwhile, dramatic changes have occurred in students’ learning preferences, digital competences, and expectations, alongside advancements in software architectures, internet accessibility, and Quality-of-Service (QoS). This paper presents the design, implementation and applications of the new version of oscilloscope simulator, named Oscilloscope Web Interface, built from scratch to replicate a real oscilloscope and signal generator. The application offers two operating modes, Simulation Mode and Acquisition & Control Mode, designed to support various use cases, including (i) supporting students’ (e-)learning of oscilloscope basics; (ii) enhancing live in-class teaching and demonstrations; (iii) creating instructional materials; (iv) supporting remote experimentation and circuit signal analysis; and (v) complementing or substituting traditional lab work. Recently, this tool was used by approximately 250 students enrolled in the Circuit Theory (TCIRC) course (ECE degree, first year, second semester). It was initially employed for off-class preparation of a laboratory script focusing on the fundamental operations of the oscilloscope and signal generator and, subsequently, for training ahead of their first laboratory test. Analysis of nearly two hundred questionnaire responses indicates that the overall user experience was highly positive. Beyond immediate classroom applications, the tool offers the potential to expand remote education capabilities, foster self-directed learning, and serve as a benchmark for developing similar tools in other engineering disciplines. Full article

The growing demand for accessible and effective e-learning platforms has led to an increased focus on innovative solutions to address the challenges posed by the diverse linguistic backgrounds of learners. This paper explores the use of AI-generated virtual speakers to enhance multilingual e-learning experiences. This study employs a system developed using Google Sheets and Google Script to create and manage multilingual courses, integrating AI-powered virtual speakers to deliver content in learners’ native languages. The e-learning platform used is a customized Moodle, and three courses were developed: “Mental Wellbeing in Mining”, “Rescue in the Mine”, and “Risk Assessment” for a European ERASMUS+ project. This study involved 147 participants from various educational and professional backgrounds. The main findings indicate that AI-generated virtual speakers significantly improve the accessibility of e-learning content. Participants preferred content in their native language and found AI-generated videos effective and engaging. This study concludes that AI-generated virtual speakers offer a promising approach to overcoming linguistic barriers in e-learning, providing personalized and adaptive learning experiences. Future research should focus on addressing ethical considerations, such as data privacy and algorithmic bias, and expanding the user base to include more languages and proficiency levels. Full article

Most of today’s educators are in no shortage of digital and online learning technologies available at their fingertips, ranging from Learning Management Systems such as Canvas, Blackboard, or Moodle, online meeting tools, online homework, and tutoring systems, exam proctoring platforms, computer simulations, and even virtual reality/augmented reality technologies. Furthermore, with the rapid development and wide availability of generative artificial intelligence (GenAI) services such as ChatGPT, we are just at the beginning of harnessing their potential to transform higher education. Yet, facing the large number of available options provided by cutting-edge technology, an imminent question on the mind of most educators is the following: how should I choose the technologies and integrate them into my teaching process so that they would best support student learning? We contemplate over these types of important and timely questions and share our reflections on evidence-based approaches to harnessing digital learning tools using a Self-regulated Engaged Learning Framework we have employed in our research in physics education that can be valuable for educators in other disciplines. Full article

Data analysis can unearth important insights like patterns, trends, and deductions. In education, it can be utilized to tailor teaching methods to suit student traits or devise new activities to foster different skills or reinforce existing ones, for example. Understanding the particular context and past experiences can aid in this endeavor. Surveys and questionnaires yield a wealth of data. Yet, the sheer volume of questions can lead to challenges in data management for teachers and a decline in student interest due to the time-consuming nature of fulfilling these tasks. This work presents a methodology designed to decrease the number of questions in questionnaires. This method can be applied to general questionnaires that consist of closed-ended questions with a set number of response choices, where each question can have a varying number of options compared to the other questions in the form. This methodology has been adapted into a newly developed software tool for examining learning styles based on a specific learning styles questionnaire: the Honey–Alonso Learning Styles Questionnaire (CHAEA). This software is available to the public and integrated with Moodle, arguably the most extensively used learning management system globally. To evaluate the effectiveness of the proposed method, it has been used across various subjects in Computer Sciences Engineering degrees over different academic years. The outcomes from this case study validate the appropriateness of the technique. Consequently, these findings could establish patterns that could assist in devising more suitable learning methodologies for students. Full article

Moodle quizzes are a convenient way of online assessment, benefiting both teachers and students. Unfortunately, their preparation is slow, cumbersome, and error-prone. In addition to the effort of designing the questions, it is necessary to enter them in Moodle. Furthermore, for questions that require images, these must first be created and then uploaded to Moodle. If this process has to be repeated with many questions, the required work becomes prohibitive. This paper presents a tool that solves all these problems, allowing the automatic and massive generation of questions for the evaluation of the knowledge about lexical analysis algorithms. The evaluation of these algorithms is relevant in the subjects of both automata and formal languages and in those of compiler design. The tool allows the creation of exercises of configurable complexity, after which the text, tables, and associated images are generated for quick upload to Moodle. The main impact of this tool is the substantial reduction of question preparation time, reducing hundreds or thousands of interactions with Moodle forms to just a few simple steps. In addition, the ease of question generation makes it possible to prepare self-assessment questionnaires for the students, something that they greatly appreciate as a study aid. 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

Knowing how students engage with their materials helps educators design methodologies to enhance the learning process. To help teachers in determining students’ average learning style profiles and analyzing potential connections between learning styles and academic performance, new freely accessible software, integrated with Moodle, has been developed in this work. A comprehensive case study involving one thousand students across various subjects and degrees from several branches of knowledge in different years has been conducted using this tool. The analysis reveals low participation in the survey, with only 6 out of 19 courses having a participation rate that surpasses 50%), where respondents generally achieved higher grades compared to non-participants (on a grade scale from 0 to 10, participants scored on average one point higher than those who did not complete the questionnaire), suggesting that they were more motivated. Higher-level courses have higher participation rates, but overall participation has declined over the years. Despite slight differences in learning styles between courses, the general pattern remains similar. The correlation between learning styles and grades, analyzed through linear regression, shows that this model performs better when both learning styles and grade marks are distributed by score rather than by level. Full article

The use of serious games on virtual learning platforms as a learning support resource is increasingly common. They are especially effective in helping students acquire mainly applied curricular content. However, a process is required to monitor the effectiveness and students’ perceived satisfaction. The objectives of this study were to (1) identify the most significant characteristics; (2) determine the most relevant predictors of learning outcomes; (3) identify groupings with respect to the different serious game activities; and (4) to determine students’ perceptions of the usefulness of the simple and complex serious game activities. We worked with a sample of 130 university students studying health sciences and biomedical engineering. The serious game activities were applied in a Moodle environment, UBUVirtual, and monitored using the UBUMonitor tool. The degree type and the type of serious game explained differing percentages of the variance in the learning results in the assessment tests (34.4%—multiple choice tests [individual assessment]; 11.2%—project performance [group assessment]; 25.6%—project presentation [group assessment]). Different clusters were found depending on the group of students and the algorithm applied. The Adjusted Rang Index was applied to determine the most appropriate algorithm in each case. The student satisfaction was high in all the cases. However, they indicated complex serious games as being more useful than simple serious games as learning resources for the practical content in both health sciences and biomedical engineering degrees. Full article

Digital transformation has become a critical aspect of modern education, necessitating the development of digital skills among all students, including those with disabilities. Among these, blind students face unique challenges in acquiring the digital competencies needed for academic success and professional integration. This study aimed to enhance the digital transformation skills of blind postgraduate students by evaluating the effectiveness of a cloud-based learning management system, Moodle Cloud. Using a mixed methods approach, we combined descriptive and quasi-experimental designs to assess the impact of the intervention. The sample included 20 blind graduate students from Beni Suef University, equally divided into experimental and control groups. Pre- and post-assessments measured participants’ digital transformation skills through achievement tests and performance evaluations. The findings indicated significant improvements in the experimental group, with higher scores in both the achievement tests and performance assessments compared to the control group. The results suggest that the cloud-based learning management system played a vital role in enhancing digital skills, and no significant differences were found between remote and in-person applications of the intervention. The study emphasizes the importance of incorporating modern digital technologies into the education of blind students, aligning with Egypt’s Vision 2030 plan and ongoing educational reforms. Full article