MoocCast: evaluating mobile-screencast for online courses

Tabuenca, Bernardo; Kalz, Marco; Löhr, Ansje

doi:10.1007/s10209-017-0528-x

Cited by 11 publications

(11 citation statements)

References 16 publications

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“…The rise of digital video worldwide may be one of the causes of the exponential growth of MOOCs [41]. The evaluations of MOOCs and their latest technological advances suggest a greater interaction and collaboration using multimedia [24]. The MOOC on Marine Litter comprises different digital instruments such as videos, interactive maps, social networks, group tasks, and discussion forums.…”

Section: Discussionmentioning

confidence: 99%

“…Many academics become interested in MOOCs recognizing their potential to offer environmental education for sustainability [19][20][21] on an unprecedented scale [22,23]. In addition, the proliferation of mobile devices in recent years is facilitating access to educational content [24][25][26][27] especially increasing participation in developing countries where there is neither habit nor infrastructures to manage waste properly [2]. The problem of marine litter is global and must be addressed via education and awareness [28,29].…”

Section: Introductionmentioning

confidence: 99%

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Massive Open Online Education for Environmental Activism: The Worldwide Problem of Marine Litter

2019

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(1) The amount of plastic discharges in the environment has drastically increased in the last decades negatively affecting aquatic ecosystems, societies, and the world economy. The policies initiated to deal with this problem are insufficient and there is an urgency to initiate local actions based on a deep understanding of the factors involved. (2) This paper investigates the potential of massive open online courses (MOOCs) to spread environmental education. Therefore, the conclusions drawn from the implementation of a MOOC to combat the problem of marine litter in the world are presented. (3) This work describes the activity of 3632 participants from 64 countries taking an active role presenting useful tools, connecting them with the main world associations, and defining applied action plans in their local area. Pre- and post-questionnaires explore behavioral changes regarding the actions of participants to combat marine litter. The role of MOOCs is contrasted with social media, formal education, and informal education. (4) Findings suggest that MOOCs are useful instruments to promote environmental activism, and to develop local solutions to global problems, for example, clean beaches, supplanting plastic bottles, educational initiatives, and prohibition of single-use plastic.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Massive Open Online Education for Environmental Activism: The Worldwide Problem of Marine Litter

2019

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“…There are thus cases in which some students passively choose online learning to meet credit requirements. Information perception refers to students' reaction to information ( 55 ). That is, students can view course announcements, the syllabus, teaching plans, and other teaching materials on the network platform and can follow the requirements to study independently to meet course requirements.…”

Section: Literature Reviewmentioning

confidence: 99%

Effects of Online Learning on College Students in Eastern China: A Structural Equation Model

Zhu

Zhao

Wang

et al. 2022

Front. Public Health

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With the spread of COVID-19 worldwide, online education is rapidly catching on, even in some underdeveloped countries and regions. Based on Bandura's ternary learning theory and literature review, this paper takes online learning of college students as the research object and conducts an empirical survey on 6,000 college students in East China. Based on literature review and factor analysis and structural equation model, this paper discusses the relationship among learning cognition, learning behavior, and learning environment in online learning of college students. The learning behavior includes interactive communication, self-discipline mechanism, classroom learning, and study after class. The learning environment includes teaching ability, knowledge system, platform support, process control, and result evaluation; learning cognition includes learning motivation, information perception, and adaptability. It is found that the learning environment has a significant positive impact on learning behavior, and learning cognition has a significant positive impact on learning behavior. It is uncertain whether the learning environment significantly impacts learning cognition. At the learning environment level, the teaching ability (0.59) has the most significant impact on the learning environment, followed by result evaluation (0.42), platform support (0.40), process control (0.33), and knowledge system (0.13). In terms of the influence on learning behavior, classroom learning has the most significant impact (0.79), followed by self-discipline mechanism (0.65), study after class (0.54), and interactive communication (0.44). In terms of learning cognition, information perception (0.62) has the most significant influence, followed by learning motivation (0.50) and adaptability (0.41). This paper suggests strengthening the construction of platforms and digital resources to create a more competitive learning environment. Improve process management and personalized online services, constantly stimulate students' enthusiasm for independent online learning, and cultivate students' online independent learning ability to promote the sustainable and healthy development of online education.

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“…Arguably, the popularity of M-learning is compelling MOOC designers to allow M-learners to take MOOC courses on mobile devices [49], [50]. Standard models of M-learning look very much like traditional classroom learning where learners are taken out from normal living environments to spend five to six hours in learning stuff which they may or may not encounter in their daily lives [51].…”

Section: Related Workmentioning

confidence: 99%

Improving M-Learners’ Performance Through Deep Learning Techniques by Leveraging Features Weights

et al. 2020

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Mobile learning (M-learning) has gained tremendous attention in the educational environment in the past decade. For effective M-learning, it is important to create an efficient M-learning model that can identify the exact requirements of mobile learners (M-learners). M-learning model is composed of features that are generated during M-learners' interaction with mobile devices. For an adaptive M-learning model, not only learning features are required, but it is also important to determine how they differ for various M-learners, their weights, and interrelationship. This study proposes a robust and adaptive Mlearning model that is based on machine learning and deep learning (ML/DL) techniques. The proposed M-learning model dynamically explores learning features, their corresponding weights, and association for M-learners. Based on learning features, the M-learning model categorizes M-learners into different performance groups. The M-learning model then provides adaptive content, suggestions, and recommendations to M-learners in order to make learning adaptive and stimulating. For comparative analysis, the prediction accuracy of five baseline ML models was compared with the deep Artificial Neural Network (deep ANN). The results demonstrated that deep ANN and Random Forest (RF) models exhibited better prediction accuracy. Subsequently, both models were selected for developing the M-learning model which included the performance categorization of M-learners under a five-level classification scheme and assigning weights to various features for providing adaptive help and support to M-learners. Our explanatory analysis has shown that behavioral features besides contextual features also influence the learning performance of M-learners. As a direct outcome of this research, more efficient, interactive, and useful mobile learning applications can be developed that accurately predict learning objectives and requirements of diverse Mlearners thus helping M-learners in enhancing their study behavior.

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MoocCast: evaluating mobile-screencast for online courses

Cited by 11 publications

References 16 publications

Massive Open Online Education for Environmental Activism: The Worldwide Problem of Marine Litter

Massive Open Online Education for Environmental Activism: The Worldwide Problem of Marine Litter

Effects of Online Learning on College Students in Eastern China: A Structural Equation Model

Improving M-Learners’ Performance Through Deep Learning Techniques by Leveraging Features Weights

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