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

Artificial Intelligence methodologies to early predict student outcome and enrich learning material

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Towards Student Engagement Analytics: Applying Machine Learning to Student Posts in Online Lecture Videos

The use of online learning environments in higher education is becoming ever more prevalent with the inception of MOOCs (Massive Open Online Courses) and the increase in online and flipped courses at universities. Although the online systems used to deliver course content make education more accessible, students often express frustration with the lack of assistance during online lecture videos. Instructors express concern that students are not engaging with the course material in online environments, and rely on affordances within these systems to figure out what students are doing. With many online learning environments storing log data about students usage of these systems, research into learning analytics, the measurement, collection, analysis, and reporting data about learning and their contexts, can help inform instructors about student learning in the online context. This thesis aims to lay the groundwork for learning analytics that provide instructors high-level student engagement data in online learning environments. Recent research has shown that instructors using these systems are concerned about their lack of awareness about student engagement, and educational psychology has shown that engagement is necessary for student success. Specifically, this thesis explores the feasibility of applying machine learning to categorize student posts by their level of engagement. These engagement categories are derived from the ICAP framework, which categorizes overt student behaviors into four tiers of engagement: Interactive, Constructive, Active, and Passive. Contributions include showing what natural language features are most indicative of engagement, exploring whether this machine learning method can be generalized to many courses, and using previous research to develop mockups of what analytics using data from this machine learning method might look like

Recommending Personalized Summaries of Teaching Materials

Teaching activities have nowadays been supported by a variety of electronic devices. Formative assessment tools allow teachers to evaluate the level of understanding of learners during frontal lessons and to tailor the next teaching activities accordingly. Despite plenty of teaching materials are available in the textual form, manually exploring these very large collections of documents can be extremely time-consuming. The analysis of learner-produced data (e.g., test outcomes) can be exploited to recommend short extracts of teaching documents based on the actual learner’s needs. This paper proposes a new methodology to recommend summaries of potentially large teaching documents. Summary recommendations are customized to student’s needs according to the results of comprehension tests performed at the end of frontal lectures. Specifically, students undergo multiple-choice tests through a mobile application. In parallel, a set of topic-specific summaries of the teaching documents is generated. They consist of the most significant sentences related to a specific topic. According to the results of the tests, summaries are personally recommended to students. We assessed the applicability of the proposed approach in real context, i.e., a B.S. university-level course. The results achieved in the experimental evaluation confirmed its usability

Video Augmentation in Education: in-context support for learners through prerequisite graphs

The field of education is experiencing a massive digitisation process that has been ongoing for the past decade. The role played by distance learning and Video-Based Learning, which is even more reinforced by the pandemic crisis, has become an established reality. However, the typical features of video consumption, such as sequential viewing and viewing time proportional to duration, often lead to sub-optimal conditions for the use of video lessons in the process of acquisition, retrieval and consolidation of learning contents. Video augmentation can prove to be an effective support to learners, allowing a more flexible exploration of contents, a better understanding of concepts and relationships between concepts and an optimization of time required for video consumption at different stages of the learning process. This thesis focuses therefore on the study of methods for: 1) enhancing video capabilities through video augmentation features; 2) extracting concept and relationships from video materials; 3) developing intelligent user interfaces based on the knowledge extracted. The main research goal is to understand to what extent video augmentation can improve the learning experience. This research goal inspired the design of EDURELL Framework, within which two applications were developed to enable the testing of augmented methods and their provision. The novelty of this work lies in using the knowledge within the video, without exploiting external materials, to exploit its educational potential. The enhancement of the user interface takes place through various support features among which in particular a map that progressively highlights the prerequisite relationships between the concepts as they are explained, i.e., following the advancement of the video. The proposed approach has been designed following a user-centered iterative approach and the results in terms of effect and impact on video comprehension and learning experience make a contribution to the research in this field

A Semantics-based User Interface Model for Content Annotation, Authoring and Exploration

The Semantic Web and Linked Data movements with the aim of creating, publishing and interconnecting machine readable information have gained traction in the last years. However, the majority of information still is contained in and exchanged using unstructured documents, such as Web pages, text documents, images and videos. This can also not be expected to change, since text, images and videos are the natural way in which humans interact with information. Semantic structuring of content on the other hand provides a wide range of advantages compared to unstructured information. Semantically-enriched documents facilitate information search and retrieval, presentation, integration, reusability, interoperability and personalization. Looking at the life-cycle of semantic content on the Web of Data, we see quite some progress on the backend side in storing structured content or for linking data and schemata. Nevertheless, the currently least developed aspect of the semantic content life-cycle is from our point of view the user-friendly manual and semi-automatic creation of rich semantic content. In this thesis, we propose a semantics-based user interface model, which aims to reduce the complexity of underlying technologies for semantic enrichment of content by Web users. By surveying existing tools and approaches for semantic content authoring, we extracted a set of guidelines for designing efficient and effective semantic authoring user interfaces. We applied these guidelines to devise a semantics-based user interface model called WYSIWYM (What You See Is What You Mean) which enables integrated authoring, visualization and exploration of unstructured and (semi-)structured content. To assess the applicability of our proposed WYSIWYM model, we incorporated the model into four real-world use cases comprising two general and two domain-specific applications. These use cases address four aspects of the WYSIWYM implementation: 1) Its integration into existing user interfaces, 2) Utilizing it for lightweight text analytics to incentivize users, 3) Dealing with crowdsourcing of semi-structured e-learning content, 4) Incorporating it for authoring of semantic medical prescriptions

The Multimodal Tutor: Adaptive Feedback from Multimodal Experiences

This doctoral thesis describes the journey of ideation, prototyping and empirical testing of the Multimodal Tutor, a system designed for providing digital feedback that supports psychomotor skills acquisition using learning and multimodal data capturing. The feedback is given in real-time with machine-driven assessment of the learner's task execution. The predictions are tailored by supervised machine learning models trained with human annotated samples. The main contributions of this thesis are: a literature survey on multimodal data for learning, a conceptual model (the Multimodal Learning Analytics Model), a technological framework (the Multimodal Pipeline), a data annotation tool (the Visual Inspection Tool) and a case study in Cardiopulmonary Resuscitation training (CPR Tutor). The CPR Tutor generates real-time, adaptive feedback using kinematic and myographic data and neural networks