Adaptive Content Presentation Extension for Open edX. Enhancing MOOCs Accessibility for Users with Disabilities

In this paper, we propose a three-layer architecture to extend the Massive Open Online Courses (MOOCs) platform Open edX to enhance course content accessibility for users with disabilities. Because of their open nature and global scope, MOOCs are a great opportunity for people with disabilities that might not be able to engage in learning otherwise. The goal of the proposed extension is to enhance MOOCs’ accessibility by adapting course content to student needs, preferences, skills and situations. In this approach, the user does not need to know what adaptations should be applied to the MOOC to make it more accessible for them. The user only needs to keep updated their accessibility preferences in their user profile. The extension automatically applies all the necessary adaptations as commanded by the adaptive engine and provides the presentation layer with the content best suited for the user

A Design Methodology for Learning Analytics Information Systems: Informing Learning Analytics Development with Learning Design

The paper motivates, presents and demonstrates a methodology for developing and evaluating learning analytics information systems (LAIS) to support teachers as learning designers. In recent years, there has been increasing emphasis on the benefits of learning analytics to support learning and teaching. Learning analytics can inform and guide teachers in the iterative design process of improving pedagogical practices. This conceptual study proposed a design approach for learning analytics information systems which considered the alignment between learning analytics and learning design activities. The conceptualization incorporated features from both learning analytics, learning design, and design science frameworks. The proposed development approach allows for rapid development and implementation of learning analytics for teachers as designers. The study attempted to close the loop between learning analytics and learning design. In essence, this paper informs both teachers and education technologists about the interrelationship between learning design and learning analytics

Exploring the Characteristics of Adults’ Online Learning Activities: a Case Study of EdX Online Institute

Online learning has become a prevailing trend among adult learners. Therefore, this study investigated the learning time preference and the relationship between the course completion and learning activities among adult learners based on data from one online learning platform. Results indicate that a periodical fluctuation of participating online course study exists among adult learners. Additionally, the activity of posting on the discussion board is a main learning activity factor that influences their online course completion. It is expected that this study would help online learning system designers, education administrators and instructors to better understand the characteristics of adult learners and their learning activities to provide better accessibility and flexibility in online learning environments for them

Investigating a learning analytics interface for automatically marked programming assessments

Student numbers at the University of Cape Town continue to grow, with an increasing number of students enrolling to study programming courses. With this increase in numbers, it becomes difficult for lecturers to provide individualised feedback on programming assessments submitted by students. To solve this, the university utilises an automatic marking tool for marking assignments and providing feedback. Students can submit assignments and receive instant feedback on marks allocated or errors in their submissions. This tool saves time as lecturers spend less time on marking and provides instant feedback on submitted code, hence providing the student with an opportunity to correct errors in their submitted code. However, most students have identified areas where improvements can be made on the interface between the automatic marker and the submitted programs. This study investigates the potential of creating a learning analytics inspired dashboard interface to improve the feedback provided to students on their submitted programs. A focus group consisting of computer science class representatives was organised, and feedback from this focus group was used to create dashboard mock-ups. These mock-ups were then used to develop high-fidelity learning analytics inspired dashboard prototypes that were tested by first-year computer science students to determine if the interfaces were useful and usable. The prototypes were designed using the Python programming language and Plotly Python library. User-centred design methods were employed by eliciting constant feedback from students during the prototyping and design of the learning analytics inspired interface. A usability study was employed where students were required to use the dashboard and then provide feedback on its use by completing a questionnaire. The questionnaire was designed using Nielsen's Usability Heuristics and AttrakDiff. These methods also assisted in the evaluation of the dashboard design. The research showed that students considered a learning analytics dashboard as an essential tool that could help them as they learn to program. Students found the dashboard useful and had an overall understanding of the specific features they would like to see implemented on a learning analytics inspired dashboard used by the automatic marking tool. Some of the specific features mentioned by students include overall performance, duly performed needed to qualify for exams, highest score, assignment due dates, class average score, and most common errors. This research hopes to provide insight on how automatically marked programming assessments could be displayed to students in a way that supports learning

Diseño de arquitectura y visualizaciones para un módulo de analítica del aprendizaje en Open edX

Los cursos MOOC (Massive Online Open Course) están emergiendo con fuerza como un tipo importante de cursos online. Entre las características más importantes de estos cursos, se encuentra el carácter masivo de estos, suponiendo la generación de una gran cantidad de datos, creados a partir del registro de las interacciones de los usuarios con el curso. Interpretar correctamente estos datos, puede suponer un gran impacto en la mejora del proceso educativo, pero dada la gran cantidad de datos de bajo nivel que se recogen, analizarlos por parte de los usuarios puede resultar una tarea muy tediosa o prácticamente imposible en muchos casos. La plataforma Open edX, creada por la Universidad de Harvard y el Instituto Tecnológico de Massachusetts (MIT), con la colaboración de múltiples universidades e instituciones de todo el mundo, es una de las plataformas MOOC con más éxito en el momento, contando con más de 400 cursos y millones de usuarios. Debido al temprano grado de desarrollo de la plataform, aún no cuenta con un módulo completo que aplique técnicas de Learning Analytics para interpretar los datos que se recogen y mostrarlos de forma que los usuarios puedan interpretarlosfacilmente. El objetivo de este proyecto es, aprovechando la facilidad de desarrollo en una plataforma de código abierto, realizar una primera aproximación al diseño de un módulo de tratamiento de datos, con objeto de ofrecerlo a la comunidad para su mejora y mantenimiento, pensando, en un futuro, en su posible integración con la plataforma Open edX, de forma que cualquier usuario pueda beneficiarse de él. En este trabajo se colabora en el diseño de la arquitectura de este módulo, incluyendo una capa intermedia para obtención de datos de la plataforma, de forma que pueda ser empleada para obtener datos tanto por las visualizaciones del modulo como por otros desarrolladores que quieran hacer uso de ella. También, en este trabajo, se diseñan e implementan una serie de visualizaciones para dicho módulo, incluyendo temas como notas de los alumnos, acceso a las secciones del curso, o progreso de los alumnos en vídeos y problemas.Ingeniería de Telecomunicació

Utilizing Online Activity Data to Improve Face-to-Face Collaborative Learning in Technology-Enhanced Learning Environments

학위논문 (박사)-- 서울대학교 대학원 : 융합과학기술대학원 융합과학부(디지털정보융합전공), 2019. 2. Rhee, Wonjong .We live in a flood of information and face more and more complex problems that are difficult to be solved by a single individual. Collaboration with others is necessary to solve these problems. In educational practice, this leads to more attention on collaborative learning. Collaborative learning is a problem-solving process where students learn and work together with other peers to accomplish shared tasks. Through this group-based learning, students can develop collaborative problem-solving skills and improve the core competencies such as communication skills. However, there are many issues for collaborative learning to succeed, especially in a face-to-face learning environment. For example, group formation, the first step to design successful collaborative learning, requires a lot of time and effort. In addition, it is difficult for a small number of instructors to manage a large number of student groups when trying to monitor and support their learning process. These issues can amount hindrance to the effectiveness of face-to-face collaborative learning. The purpose of this dissertation is to enhance the effectiveness of face-to-face collaborative learning with online activity data. First, online activity data is explored to find whether it can capture relevant student characteristics for group formation. If meaningful characteristics can be captured from the data, the entire group formation process can be performed more efficiently because the task can be automated. Second, learning analytics dashboards are implemented to provide adaptive support during a class. The dashboards system would monitor each group's collaboration status by utilizing online activity data that is collected during class in real-time, and provide adaptive feedback according to the status. Lastly, a predictive model is built to detect at-risk groups by utilizing the online activity data. The model is trained based on various features that represent important learning behaviors of a collaboration group. The results reveal that online activity data can be utilized to address some of the issues we have in face-to-face collaborative learning. Student characteristics captured from the online activity data determined important group characteristics that significantly influenced group achievement. This indicates that student groups can be formed efficiently by utilizing the online activity data. In addition, the adaptive support provided by learning analytics dashboards significantly improved group process as well as achievement. Because the data allowed the dashboards system to monitor current learning status, appropriate feedback could be provided accordingly. This led to an improvement of both learning process and outcome. Finally, the predictive model could detect at-risk groups with high accuracy during the class. The random forest algorithm revealed important learning behaviors of a collaboration group that instructors should pay more attention to. The findings indicate that the online activity data can be utilized to address practical issues of face-to-face collaborative learning and to improve the group-based learning where the data is available. Based on the investigation results, this dissertation makes contributions to learning analytics research and face-to-face collaborative learning in technology-enhanced learning environments. First, it can provide a concrete case study and a guide for future research that may take a learning analytics approach and utilize student activity data. Second, it adds a research endeavor to address challenges in face-to-face collaborative learning, which can lead to substantial enhancement of learning in educational practice. Third, it suggests interdisciplinary problem-solving approaches that can be applied to the real classroom context where online activity data is increasingly available with advanced technologies.Abstract i Chapter 1. Introduction １ 1.1. Motivation １ 1.2. Research questions ４ 1.3. Organization ６ Chapter 2. Background ８ 2.1. Learning analytics ８ 2.2. Collaborative learning ２２ 2.3. Technology-enhanced learning environment ２７ Chapter 3. Heterogeneous group formation with online activity data ３５ 3.1. Student characteristics for heterogeneous group formation ３６ 3.2. Method ４１ 3.3. Results ５１ 3.4. Discussion ５９ 3.5. Summary ６４ Chapter 4. Real-time dashboard for adaptive feedback in face-to-face CSCL ６７ 4.1. Theoretical background ７０ 4.2. Dashboard characteristics ８１ 4.3. Evaluation of the dashboard ９４ 4.4. Discussion １０７ 4.5. Summary １１４ Chapter 5. Real-time detection of at-risk groups in face-to-face CSCL １１８ 5.1. Important learning behaviors of group in collaborative argumentation １１８ 5.2. Method １２０ 5.3. Model performance and influential features １２５ 5.4. Discussion １２９ 5.5. Summary １３２ Chapter 6. Conclusion １３４ Bibliography １４０Docto

Desarrollo de un XBlock en Open edX para apoyar las analíticas de aprendizaje

Hoy en día, cuando los usuarios utilizan los sistemas de información, generan una gran cantidad de datos que dejan un rastro como resultado de su interacción, por ejemplo, cuando los estudiantes acceden a materiales educativos en Entornos Virtuales de Aprendizaje (VLE – Virtual Learning Environments). Esto se ha convertido en lo que ahora conocemos como Cursos Masivos Abiertos en Línea (MOOC – por sus siglas en inglés), que tienen millones de estudiantes registrados. Debido a la gran cantidad de datos que se generan dentro de los MOOC, las analíticas de aprendizaje (LA – Learning Analytics) ha surgido como una alternativa para mejorar los procesos de enseñanza y aprendizaje a través del análisis de datos. Open edX en un intento de incorporar LA en su plataforma de desarrollo Insights, que proporciona visualizaciones muy simples. De igual manera, se han sumado otros proyectos que con el paso del tiempo han quedado obsoletos o no brindan el apoyo suficiente para mejorar el proceso de aprendizaje de los estudiantes. Debido a lo anterior, este trabajo de titulación propone el diseño, desarrollo y evaluación de un dashboard de analíticas de aprendizaje para la plataforma Open edX. La herramienta incorporará indicadores de éxito académico en sus visualizaciones para mejorar el proceso de aprendizaje dentro de la plataforma. Con el propósito de desarrollar la herramienta de LA, se realizó un análisis exploratorio del comportamiento de los estudiantes para determinar variables y secuencias de aprendizaje, se diseñaron visualizaciones para ser incorporadas dentro del dashboard de docentes y estudiantes. Para llevar a cabo el desarrollado del componente de LA, denominado XLEA (XBlock for LEarning Analytics), se empleó la metodología LATUX (Learning Awareness Tool – User eXperience), la cual consta de cinco etapas agrupadas en dos enfoques. Finalmente, luego de haber evaluado la herramienta XLEA con el cuestionario Evaluation Framework for Learning Analytics (EFLA) y con el cuestionario de experiencia de usuario UEQ, por estudiantes y maestros de la Universidad de Cuenca, se logró evidenciar un alto grado de aceptación y conformidad por parte de los participantes al usar XLEA. La mayoría de participantes se enfocaron en aquellas visualizaciones que permiten tener una comprensión del comportamiento histórico por semanas de las diferentes actividades que desempeñan dentro del curso, por ejemplo, videos, lecturas y problemasNowadays, when users use information systems, they generate a large amount of data that leaves a trace as a result of their interaction, for example, when students access educational materials in Virtual Learning Environments (VLE). This has evolved into what we now know as Massive Open Online Courses (MOOC), which have millions of students registered. Due to the large amount of data generated within MOOC, Learning Analytics (LA) has emerged as an alternative to improve teaching and learning processes through data analysis. Open edX in an attempt to incorporate LA into its Insights development platform, which provides very simple visualizations. Similarly, other projects have been added that have become obsolete over time or do not provide sufficient support to improve the student learning process. Due to the above, this degree work proposes the design, development and evaluation of a learning analytics dashboard for the Open edX platform. The tool will incorporate indicators of academic success in its visualizations to improve the learning process within the platform. With the purpose of developing the LA tool, an exploratory analysis of student behavior was carried out to determine variables and learning sequences, visualizations were designed to be incorporated into the dashboard of teachers and students. To carry out the development of the LA component, called XLEA (XBlock for LEarning Analytics), the LATUX (Learning Awareness Tool – User eXperience) methodology was used, which consists of five stages grouped into two approaches. Finally, after having evaluated the XLEA tool with the Evaluation Framework for Learning Analytics (EFLA) questionnaire and with the UEQ user experience questionnaire, by students and teachers of the University of Cuenca, a high degree of acceptance and conformity was evidenced. by participants when using XLEA. Most of the participants focused on those visualizations that allow an understanding of the historical behavior for weeks of the different activities that they carry out within the course, for example, videos, readings and problemsIngeniero de SistemasCuenc

Supporting engagement in active video watching using quality nudges and visualisations

Learning by watching videos has been a popular method in e-learning. However, developing and maintaining constructive engagement is a crucial challenge in video-based learning (VBL). AVW-Space is an online VBL platform that enhances student engagement by providing note- taking and peer-reviewing. Previous studies with AVW-Space showed higher learning outcomes for students who write high-quality comments. Furthermore, an earlier study on AVW-Space suggested that visualising the student progress could help learners monitor and regulate their learning. Thus, this research aimed to increase engagement in AVW-Space by offering 1) personalised prompts, named Quality nudges, to encourage writing better comments and 2) visualisations of the student model to facilitate monitoring and controlling learning. I conducted a series of studies to investigate the effectiveness of Quality nudges and visualisations on the students’ engagement and learning. Firstly, I automated the assessment of comments quality using machine learning approaches. Then, I developed Quality nudges which encourage students to write better comments by triggering critical thinking and self-reflection. Next, I conducted a study in the context of presentation skills to analyse the effectiveness of the Quality nudges. The results showed that Quality nudges improved the quality of comments and increased learning consequently. After adding new visual learning analytics to AVW- Space, I investigated the effectiveness of the visualisations by conducting another study in the context of presentation skills. The results showed that the visualisations enhanced constructive engagement and learning even further. I also investigated the generalisability of nudges and visualisation for another transferable skill by making Quality nudges and visualisations customisable and conducting a study in the context of communication skills. Although the results showed that students used visualisations and nudges for communication skills differently from the participants in the study on presentation skills, findings indicated these interventions were still effective in increasing the quality of comments and enhancing constructive behaviour and learning. This research contributes to the development of intelligent learning environments which provide personalised interventions to encourage constructive commenting behaviours during video-based learning. The interventions proposed in this research can be applied to other domains which involve critical thinking and self-reflection. Another contribution of this research is providing visual learning analytics for students in VBL platforms to increase learning awareness and engagement. The nudges and visualisations proposed in this research could be applied to any other video-based learning platform that allows commenting

TOWARDS A LEARNING ANALYTICS REFERENCE FRAMEWORK TO PREDICT AT-RISK STUDENTS AT THE FACULTY OF MILITARY SCIENCE

School for Geospatial Studies and Information System