A Closer Look into Recent Video-based Learning Research: A Comprehensive Review of Video Characteristics, Tools, Technologies, and Learning Effectiveness

People increasingly use videos on the Web as a source for learning. To support this way of learning, researchers and developers are continuously developing tools, proposing guidelines, analyzing data, and conducting experiments. However, it is still not clear what characteristics a video should have to be an effective learning medium. In this paper, we present a comprehensive review of 257 articles on video-based learning for the period from 2016 to 2021. One of the aims of the review is to identify the video characteristics that have been explored by previous work. Based on our analysis, we suggest a taxonomy which organizes the video characteristics and contextual aspects into eight categories: (1) audio features, (2) visual features, (3) textual features, (4) instructor behavior, (5) learners activities, (6) interactive features (quizzes, etc.), (7) production style, and (8) instructional design. Also, we identify four representative research directions: (1) proposals of tools to support video-based learning, (2) studies with controlled experiments, (3) data analysis studies, and (4) proposals of design guidelines for learning videos. We find that the most explored characteristics are textual features followed by visual features, learner activities, and interactive features. Text of transcripts, video frames, and images (figures and illustrations) are most frequently used by tools that support learning through videos. The learner activity is heavily explored through log files in data analysis studies, and interactive features have been frequently scrutinized in controlled experiments. We complement our review by contrasting research findings that investigate the impact of video characteristics on the learning effectiveness, report on tasks and technologies used to develop tools that support learning, and summarize trends of design guidelines to produce learning video

Analysis of Students’ Behavior Watching iMooX Courses with Interactive Elements

Digital learning technologies are becoming increasingly important for our modern educational system. In addition to teaching methods that incorporate interactivity, these approaches benefit students’ overall learning experience and success by enhancing their attention and fostering a positive attitude towards the learning content being presented. Interactivity comes in various forms, and while a combination of distinct activities is beneficial, some are more effective at engaging students. Using digital technologies in an educational environment opens up new possibilities for students, teachers, and researchers. It provides new insights into learning behavior and enables the collection of interaction information. This data could, for example, show how often a video was paused or at what point students lost interest and left, but gaining such knowledge requires further processing. The use of visualizations that depict behavior, such as the change of attention over time, can be an effective way to present extracted information. Therefore, our research focuses on developing an application that enables us to generate various visualizations from the collected data. A single command-line input will be sufficient to create them. Furthermore, a video course was created from which we collected behavioral data. Our results aim to showcase the benefits of interactivity, and that the created figures can be used for data evaluation verifies the versatility of the generated visualizations

A LEARNER INTERACTION STUDY OF DIFFERENT ACHIEVEMENT GROUPS IN MPOCS WITH LEARNING ANALYTICS TECHNIQUES

The purpose of this study was to conduct data-driven research by employing learning analytics methodology and Big Data in learning management systems (LMSs), and then to identify and compare learners’ interaction patterns in different achievement groups through different course processes in Massive Private Online Courses (MPOCs). Learner interaction is the foundation of a successful online learning experience. However, the uncertainties about the temporal and sequential patterns of online interaction and the lack of knowledge about using dynamic interaction traces in LMSs have prevented research on ways to improve interactive qualities and learning effectiveness in online learning. Also, most research focuses on the most popular online learning organization form, Massive Open Online Courses (MOOCs), and little online learning research has been conducted to investigate learners’ interaction behaviors in another important online learning organization form: MPOCs. To fill these needs, the study pays attention to investigate the frequent and effective interaction patterns in different achievement groups as well as in different course processes, and attaches importance to LMS trace data (log data) in better serving learners and instructors in online learning. Further, the learning analytics methodology and techniques are introduced here into online interaction research. I assume that learners with different achievements express different interaction characteristics. Therefore, the hypotheses in this study are: 1) the interaction activity patterns of the high-achievement group and the low-achievement group are different; 2) in both groups, interaction activity patterns evolve through different course processes (such as the learning process and the exam process). The final purpose is to find interaction activity patterns that characterize the different achievement groups in specific MPOCs courses. Some learning analytics approaches, including Hidden Markov models (HMMs) and other related measures, are taken into account to identify frequently occurring interaction activity sequence patterns of High/Low achievement groups in the Learning/Exam processes under MPOCs settings. The results demonstrate that High-achievement learners especially focused on content learning, assignments, and quizzes to consolidate their knowledge construction in both Learning and Exam processes, while Low-achievement learners significantly did not perform the same. Further, High-achievement learners adjusted their learning strategies based on the goals of different course processes; Low-achievement learners were inactive in the learning process and opportunistic in the exam process. In addition, despite achievements or course processes, all learners were most interested in checking their performance statements, but they engaged little in forum discussion and group learning. In sum, the comparative analysis implies that certain interaction patterns may distinguish the High-achievement learners from the Low-achievement ones, and learners change their patterns more or less based on different course processes. This study provides an attempt to conduct learner interaction research by employing learning analytics techniques. In the short term, the results will give in-depth knowledge of the dynamic interaction patterns of MPOCs learners. In the long term, the results will help learners to gain insight into and evaluate their learning, help instructors identify at-risk learners and adjust instructional strategies, help developers and administrators to build recommendation systems based on objective and comprehensive information, all of which in turn will help to improve the achievements of all learner groups in specific MPOC courses

Transition to online learning by a teacher education program with limited 4IR affordances

This study used the community of inquiry (CoI) framework and the affordances of Internet of things (IoT) to explore how the faculty of education of a rural campus of a university transitioned from face-to-face to online modes of instruction. In this qualitative interpretive study, data were collected through open-ended questionnaires from four purposely selected teacher educators and five final-year bachelor of education preservice teachers. Thematic content analysis techniques were used to analyze the data collected. The findings indicated the tensions experienced by the teacher educators as they negotiated the limited IoT affordances to ensure effective teaching, cognitive, and social presence in the newly formed online classrooms. Some of the tensions included the choices that were made on whether to use synchronous or asynchronous modes of instruction and the selection of effective communication modes. The teacher educators used a combination of the official learning management system (LMS) tools of the university and a social media platform as way of navigating the limited 4IR environments experienced by the preservice teachers

Integration of Reciprocal Teaching-ICT Model To Improve Students’Mathematics Critical Thinking Ability

This research examines the effectiveness on how mathematics teachers have begun to integrate information and communication technology (ICT) with reciprocal teaching model to improve students’ mathematics critical thinking ability into seventh junior high school classroom practice. This study was experimental research with a quasi-experimental design. The samples of the study are 36 students for classroom experiments and 36 students for classroom control. The instruments employed in this study were pre-test and post-test. All the instruments are made in essays forms. The data were analyzed by using descriptive statistics. Based on the research findings, it was gotten that (1) the development of teaching instructional multimedia of the seven grade students of junior high school; (2) the improvement of students’ mathematics critical thinking ability in experimental class; (3) the aspect of attractiveness shows that the developed instructional multimedia was very interesting; and (4) reciprocal learning has good impact on students’ mathematics critical thinking ability

Improvement of Student Attention Monitoring Supported by Precision Sensing in Learning Management Systems

A Learning Management Systems (LMS) can benefit from the inclusion Computer-Mediated-Communications (CMC) software for delivering materials. Incorporating CMC tools in virtual classrooms or implementing educational blogs, can be very effective in e-learning platforms. In such student-centered interaction scenarios, it is important to monitor and manage student attention in a precise way to enhance student performance. Sensing with precision through 6G/7G technology allows to include electronic and software devices to produce such monitoring. This chapter contextualizes and describes an abstraction application scenario of sensing and monitoring student attention with high precision in Learning Management System with new communication systems. In that context, technology (e.g. sensors), is used to perform automatic attention monitoring, helping to manage students in e-Learning. Additionally, the document presents a possible scenario which supports intelligent services to the monitoring of student attention during e-learning activities in the context of Smart HEI (Higher Education Institutes)

Tools and techniques for exploring engagement in a flipped classroom

PhD ThesisStudent engagement has been linked to satisfaction, retention, and better student performance. It is considered a factor and a goal for enhancing the students experience in higher education. However, engagement is a complex construct that includes multiple aspects that is influenced by learning environment, teaching staff, and the students’ background and expectations. There are many studies investigating different learning environments and exploring various aspects of engagement like behaviour and emotion. One area that has not been sufficiently researched is the flipped classroom, which is a blended learning model publicized as an approach to improve engagement. Thus, this thesis offers an in-depth exploration of student engagement in Higher Education flipped classroom and identify indicators and measures that can be used to understand as well as improve engagement in this context. In addition, this study investigates the role of technology-enhanced reflection in improving engagement in class and introduces a new tool designed to support reflection in small group activities. The study was conducted in a third-year module on Ubiquitous Computing in the School of Computing Science. The module was a fully flipped course where students prepared before class by going through the provided material on the website. In class, students engaged in small group activities to expand on what they learned online. Data was collected from the website, class observations (forms and video), and interviews with students and the module leader. In addition, a web app called Group Tagging was developed as a tool to support reflection using video tagging was tested to investigate whether this type of reflection can improve student engagement in class. Across the two case studies conducted as a part of this research on a flipped classroom, I identified a range of measures and methods that can be used to increase students’ engagement. The study showed that engagement in the flipped classroom could change depending on the structure of the course, clear communications with the students, the teaching staff’s continued engagement and giving the students an incentive and motivation to keep doing the work required. The findings also showed that video supported-reflection helped the students stay on task and participate more in class. It also worked as an incentive for them to be better prepared before coming to class