VLEngagement: A Dataset of Scientific Video Lectures for Evaluating Population-based Engagement

With the emergence of e-learning and personalised education, the production and distribution of digital educational resources have boomed. Video lectures have now become one of the primary modalities to impart knowledge to masses in the current digital age. The rapid creation of video lecture content challenges the currently established human-centred moderation and quality assurance pipeline, demanding for more efficient, scalable and automatic solutions for managing learning resources. Although a few datasets related to engagement with educational videos exist, there is still an important need for data and research aimed at understanding learner engagement with scientific video lectures. This paper introduces VLEngagement, a novel dataset that consists of content-based and video-specific features extracted from publicly available scientific video lectures and several metrics related to user engagement. We introduce several novel tasks related to predicting and understanding context-agnostic engagement in video lectures, providing preliminary baselines. This is the largest and most diverse publicly available dataset to our knowledge that deals with such tasks. The extraction of Wikipedia topic-based features also allows associating more sophisticated Wikipedia based features to the dataset to improve the performance in these tasks. The dataset, helper tools and example code snippets are available publicly at https://github.com/sahanbull/context-agnostic-engagemen

Predicting Engagement in Video Lectures

The explosion of Open Educational Resources (OERs) in the recent years creates the demand for scalable, automatic approaches to process and evaluate OERs, with the end goal of identifying and recommending the most suitable educational materials for learners. We focus on building models to find the characteristics and features involved in context-agnostic engagement (i.e. population-based), a seldom researched topic compared to other contextualised and personalised approaches that focus more on individual learner engagement. Learner engagement, is arguably a more reliable measure than popularity/number of views, is more abundant than user ratings and has also been shown to be a crucial component in achieving learning outcomes. In this work, we explore the idea of building a predictive model for population-based engagement in education. We introduce a novel, large dataset of video lectures for predicting context-agnostic engagement and propose both cross-modal and modality-specific feature sets to achieve this task. We further test different strategies for quantifying learner engagement signals. We demonstrate the use of our approach in the case of data scarcity. Additionally, we perform a sensitivity analysis of the best performing model, which shows promising performance and can be easily integrated into an educational recommender system for OERs.Comment: In Proceedings of International Conference on Educational Data Mining 202

MoodleMiner: Data Mining Analysis Tool for Moodle Learning Management System

The purpose of this study is to develop a tool through which non-experts can carry out basic data mining analyses on logs they obtained via Moodle Learning Management System. The study also includes the findings obtained by applying the developed tool on a data set from a real course. The developed tool automatically extracts the features regarding student interactions with the learning system by using their click-stream data, and analyzes this data by using the data mining libraries available in the R programming language. The tool has enabled the users who do not have any expertise in data mining or programming to automatically carry out data mining analyses. The information generated by the tool will help researchers and educators alike in grouping students by their interaction levels, determining at-risk students, monitoring students' interaction levels, and identifying important features that impact students’ academic performances. The data processed by the tool can also be exported to be used in various other analyses. In the future versions of the tool, it is planned to add different analyzes such as association rule mining, sequential pattern mining etc

Design and evaluation of a case-based system for modelling exploratory learning behaviour of math generalisation

Exploratory learning environments (ELEs) promote a view of learning that encourages students to construct and/or explore models and observe the effects of modifying their parameters. The freedom given to learners in this exploration context leads to a variety of learner approaches for constructing models and makes modelling of learner behaviour a challenging task. To address this issue, we propose a learner modelling mechanism for monitoring learners’ actions when constructing/exploring models by modelling sequences of actions reflecting different strategies in solving a task. This is based on a modified version of case-based reasoning for problems with multiple solutions. In our formulation, approaches to explore the task are represented as sequences of simple cases linked by temporal and dependency relations, which are mapped to the learners’ behaviour in the system by means of appropriate similarity metrics. This paper presents the development and validation of the modelling mechanism. The model was validated in the context of an ELE for mathematical generalisation using data from classroom sessions and pedagogically-driven learning scenarios

Learning Sciences Beyond Cognition: Exploring Student Interactions in Collaborative Problem Solving

Composed of insightful essays from top figures in their respective fields, the book also shows how a thorough understanding of this critical discipline all but ensures better decision making when it comes to education

The sequence matters: A systematic literature review of using sequence analysis in Learning Analytics

Describing and analysing sequences of learner actions is becoming more popular in learning analytics. Nevertheless, the authors found a variety of definitions of what a learning sequence is, of which data is used for the analysis, and which methods are implemented, as well as of the purpose and educational interventions designed with them. In this literature review, the authors aim to generate an overview of these concepts to develop a decision framework for using sequence analysis in educational research. After analysing 44 articles, the conclusions enable us to highlight different learning tasks and educational settings where sequences are analysed, identify data mapping models for different types of sequence actions, differentiate methods based on purpose and scope, and identify possible educational interventions based on the outcomes of sequence analysis.Comment: Submitted to the Journal of Learning Analytic

Semantic model for mining e-learning usage with ontology and meaningful learning characteristics

The use of e-learning in higher education institutions is a necessity in the learning process. E-learning accumulates vast amount of usage data which could produce a new knowledge and useful for educators. The demand to gain knowledge from e-learning usage data requires a correct mechanism to extract exact information. Current models for mining e-learning usage have focused on the activities usage but ignored the actions usage. In addition, the models lack the ability to incorporate learning pedagogy, leading to a semantic gap to annotate mining data towards education domain. The other issue raised is the absence of usage recommendation that refers to result of data mining task. This research proposes a semantic model for mining e-learning usage with ontology and meaningful learning characteristics. The model starts by preparing data including activity and action hits. The next step is to calculate meaningful hits which categorized into five namely active, cooperative, constructive, authentic, and intentional. The process continues to apply K-means clustering analysis to group usage data into three clusters. Lastly, the usage data is mapped into ontology and the ontology manager generates the meaningful usage cluster and usage recommendation. The model was experimented with three datasets of distinct courses and evaluated by mapping against the student learning outcomes of the courses. The results showed that there is a positive relationship between meaningful hits and learning outcomes, and there is a positive relationship between meaningful usage cluster and learning outcomes. It can be concluded that the proposed semantic model is valid with 95% of confidence level. This model is capable to mine and gain insight into e-learning usage data and to provide usage recommendation

O papel dos jogos no envolvimento dos estudantes

O presente estudo tem como objetivo fornecer uma revisão abrangente da literatura sobre gamificação, jogos sérios e envolvimento dos alunos, a fim de sintetizar a informação disponível e determinar o papel da gamificação e da implementação de jogos no processo de aprendizagem para aumentar o envolvimento dos alunos. O estudo examinará o contexto da gamificação e dos jogos sérios na educação, explorará o problema do desinteresse dos alunos nas abordagens de aprendizagem tradicionais e investigará a forma como a gamificação e a aprendizagem baseada em jogos podem resolver este problema. A questão de investigação que orienta este estudo é: "Qual é o papel da gamificação e da implementação de jogos no processo de aprendizagem no envolvimento e motivação dos alunos?" Para responder à questão de investigação, será efetuada uma revisão narrativa da literatura. Estudos, artigos e publicações relevantes serão reunidos e analisados para identificar as principais conclusões e tendências relativas à eficácia da gamificação e da aprendizagem baseada em jogos no reforço do envolvimento dos alunos. A metodologia envolverá a realização de um questionário abrangente que avaliará as perceções dos alunos e dos professores sobre a gamificação e a implementação de jogos no processo de aprendizagem, bem como o impacto dos jogos na aprendizagem, e analisará os dados extraídos. Espera-se que os principais resultados deste estudo forneçam informações sobre os benefícios e desafios da utilização da gamificação e da aprendizagem baseada em jogos na educação. Prevê-se que os resultados demonstrem o impacto positivo da gamificação no envolvimento, motivação e resultados de aprendizagem dos alunos. Além disso, o estudo explorará fatores que contribuem para a implementação bem-sucedida de estratégias de gamificação, como a conceção dos elementos do jogo, o alinhamento da mecânica do jogo com os objetivos de aprendizagem e a importância de incorporar feedback e recompensas. A investigação também irá lançar luz sobre potenciais limitações e barreiras à utilização eficaz da gamificação na sala de aula. Este estudo tem implicações para educadores, designers instrucionais e decisores políticos no domínio da educação. Os resultados informarão o desenvolvimento de melhores práticas e diretrizes para a incorporação da gamificação e da aprendizagem baseada em jogos em contextos educativos, com o objetivo de aumentar o envolvimento dos alunos e promover experiências de aprendizagem significativas.The present study aims to provide a comprehensive review of the literature on gamification, serious games, and student engagement in order to synthesize the available information and determine the role of gamification and game implementation in the learning process to increase student engagement. The study will examine the context of gamification and serious games in education, explore the problem of student disengagement in traditional learning approaches, and investigate how gamification and game-based learning can address this issue. The research question that guides this study is: "What is the role of gamification and the implementation of games in the learning process on student engagement and motivation?" To address the research question, a systematic review of the literature will be conducted. Relevant studies, articles, and publications will be gathered and analyzed to identify key findings and trends regarding the effectiveness of gamification and game-based learning in enhancing student engagement. The methodology will involve conducting a comprehensive questionaire evaluating student and teacher perceptions on gamification and the implementation of games in the learning process as well as the impact of games on learning and analyzing the data extracted from these. The main results of this study are expected to provide insights into the benefits and challenges of using gamification and game-based learning in education. It is anticipated that the findings will demonstrate the positive impact of gamification on student engagement, motivation, and learning outcomes. Additionally, the study will explore factors that contribute to the successful implementation of gamification strategies, such as the design of game elements, the alignment of game mechanics with learning objectives, and the importance of incorporating feedback and rewards. The research will also shed light on potential limitations and barriers to the effective use of gamification in the classroom. This study has implications for educators, instructional designers, and policymakers in the field of education. The findings will inform the development of best practices and guidelines for incorporating gamification and game-based learning into educational settings, with the aim of enhancing student engagement and promoting meaningful learning experiences