Examining the use of instructional video clips for teaching macroeconomics

Expósito, Alfonso; Sánchez-Rivas, Javier; Gómez-Calero, María de la Palma; Gil-Delgado, María del Pópulo Pablo-Romero

doi:10.1016/j.compedu.2019.103709

Cited by 39 publications

(26 citation statements)

References 43 publications

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“…Hinsichtlich der Länge waren alle veröffentlichten Videos hauptsächlich durchschnittlich maximal zwischen fünf (5) und sieben (7) Minuten lang. Diese Dauer basiert auf Schlussfolgerungen verschiedener Studien (Expósito et al, 2020;Guo, Kim and Rubin, 2014;Van der Meij, 2017). Diese Zeitlängen ermöglichen es den Studierenden, sich bis zum Ende der Videos mit den Bildungsinhalten zu beschäftigen, die ihre Aufmerksamkeit und Konzentration verlangen.…”

Section: Erstellen Von Lehrvideosunclassified

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Teaching in Library and Information Studies: Creating videos to provide educational content

Boté‐Vericad

2021

Mitteilungen der VÖB

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COVID-19 has forced several changes in the teaching of Library and Information Studies. In this article we explain our experience in creating and publishing videos as educational content at the University of Barcelona. Students having to learn by distance teaching have different needs in regard to the provision of educational materials. Similarly, professors need to give the best possible experience to distance students when face-to-face courses are not possible. This leads to the question of the roles that information literacy and digital literacy play, especially for professors.

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Section: Erstellen Von Lehrvideosunclassified

“…Die Sprechgeschwindigkeit in Videos muss nicht anders sein als in einer anderen Situation. Normaler Durchschnitt sind 120 Wörter pro Minute in Spanisch (Expósito et al, 2020) und 156 in Englisch (Guo, Kim and Rubin, 2014). Untertitel in den Videos können je nach Zweck hinzugefügt werden.…”

Section: Erstellen Von Lehrvideosunclassified

Teaching in Library and Information Studies: Creating videos to provide educational content

Boté‐Vericad

2021

Mitteilungen der VÖB

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“…Indeed, blended and virtual real-time learning are progressively gaining popularity and becoming an integral part of academic curricula [3,4]. The virtual, real-time session portion of blended learning activities that use gamification as an active learning practice may be able to create an "edutainment" environment that promotes and sustains the motivation to learn [5][6][7][8]. Blended learning may even be able to provide opportunities for collaborative learning, for example, through pairing-and-sharing and group learning [9].…”

Section: Introductionmentioning

confidence: 99%

Quality Assurance Framework for the Design and Delivery of Virtual, Real-Time Courses

2021

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Designing and delivering outcome-based courses that emphasize learner-centric educational discourse and active learning is challenging, especially in online learning environments. Ensuring quality in the design and delivery of such courses in the virtual space requires a well-defined framework with key constituents that interact based on ordered sequences of events. Despite the pressing need for a quality assurance system for today’s virtual, real-time courses, such a system has not been systematically designed. A coherent quality assurance system requires a clear framework that defines the interacting constituents. This work proposes a conceptual and generic “Quality Assurance” (QA) framework, based on experiences primarily in Science, Technology, Engineering, and Mathematics (STEM) fields, for the effective design and delivery of outcome-based virtual, real-time courses that incorporate active learning practices. This Quality Assurance framework may be adjusted to serve as a blueprint that, once adjusted by institutions to accommodate their missions, guides institutions in developing or amending their policies and procedures for the design and delivery of virtual, real-time courses; in addition, such a framework is important for institutions to develop Quality Assurance systems that integrate mechanisms for continuous improvement. The proposed quality assurance framework includes three constituents: a “Teaching and Learning Support” (TLS) that trains educators on pedagogical approaches and the capabilities of the institution’s Learning Management System (LMS); an “Information and Communication Technology Support” (ICTS) that assists educators with the technologies and tools available in the learning management system; and a “Course Management System” (CMS) that encapsulates course design, delivery, and assessment; this study focuses primarily on this “Course Management System” constituent.

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“…Some previous studies in video learning analytics focused on examining learners' performance through video engagement behavior using explicit features such as views and annotations [13,24,45]. Recent research [17,22] indicated that learners are more possible to engage when video contexts: (a) are brief; (b) are in video production form (slides, khan academy style, code, etc. ); and (c) prepare interactive activities like in-video quizzes.…”

Section: Introductionmentioning

confidence: 99%

Visual analytics of video‐clickstream data and prediction of learners' performance using deep learning models in MOOCs' courses

Mubarak

Cao

Zhang

2020

Comp Applic In Engineering

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The big data stored in massive open online course (MOOC) platforms have become a posed challenge in the Learning Analytics field to analyze the learning behavior of learners, and predict their respective performance, related especially to video lecture data, since most learners view the same online lecture videos. This helps to conduct a comprehensive analysis of such behaviors and explore various learning patterns in MOOC video interactions. This paper aims at presenting a visual analysis, which enables course instructors and education experts to analyze clickstream data that were generated by learner interaction with course videos. It also aims at predicting learner performance, which is a vital decision-making problem, by addressing their issues and improving the educational process. This paper uses a long short-term memory network (LSTM) on implicit features extracted from videoclickstreams data to predict learners' performance and enable instructors to make measures for timely intervention. Results show that the accuracy rate of the proposed model is 89%-95% throughout course weeks. The proposed LSTM model outperforms baseline Deep learning (GRU) and simple recurrent neural network by accuracy of 90.30% in the "Mining of Massive Datasets" course, and the "Automata Theory" accuracy is 89%.

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Examining the use of instructional video clips for teaching macroeconomics

Cited by 39 publications

References 43 publications

Teaching in Library and Information Studies: Creating videos to provide educational content

Teaching in Library and Information Studies: Creating videos to provide educational content

Quality Assurance Framework for the Design and Delivery of Virtual, Real-Time Courses

Visual analytics of video‐clickstream data and prediction of learners' performance using deep learning models in MOOCs' courses

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