Sustainability Education in Massive Open Online Courses:  A Content Analysis Approach

Zhan, Zehui; Fong, Patrick S.W.; Mei, Hu; Chang, Xiaofeng; Liang, T. H.; Zicheng, Ma

doi:10.3390/su7032274

Cited by 56 publications

(43 citation statements)

References 31 publications

(47 reference statements)

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“…This conclusion has greatly boosted the online education of Sustainability. For example, there is none of Chinese university providing sustainability-related online courses in 2015 [13], while there are nine courses on xuetangX.com in 2017.…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Efficiency of Massive Online Learning by Integrating Intelligent Analysis into MOOCs with an Application to Education of Sustainability

Zhou

2018

Sustainability

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Massive Open Online Courses (MOOCs) is an innovative method in modern education, especially important for autonomous study and the sharing of global excellent education resources. However, it is not easy to implement the teaching process according to the specific characters of students by MOOCs because the number of participants is huge and the teacher cannot identify the characters of students through a face to face interaction. As a new subject combined with different areas, such as economics, sociology, environment, and even engineering, the education of sustainability-related courses requires elaborate consideration of individualized teaching for students from diverse backgrounds and with different learning styles. Although the major MOOC platforms or learning management systems (LMSs) have tried lots of efforts in the design of course system and the contents of the courses for sustainability education, the achievements are still unsatisfied, at least the issue of how to effectively take into account the individual characteristics of participants remains unsolved. A hybrid Neural Network (NN) model is proposed in this paper which integrates a Convolutional Neural Networks (CNN) and with a Gated Recurrent Unit (GRU) based Recurrent Neural Networks (RNN) in an effort to detect individual learning style dynamically. The model was trained by learners' behavior data and applied to predicting their learning styles. With identified learning style for each learner, the power of MOOC platform can be greatly enhanced by being able to offer the capabilities of recommending specific learning path and the relevant contents individually according to their characters. The efficiency of learning can thus be significantly improved. The proposed model was applied to the online study of sustainability-related course based on a MOOC platform with more than 9,400,000 learners. The results revealed that the learners could effectively increase their learning efficiency and quality for the courses when the learning styles are identified, and proper recommendations are made by using our method.

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Section: Introductionmentioning

confidence: 99%

Enhancing the Efficiency of Massive Online Learning by Integrating Intelligent Analysis into MOOCs with an Application to Education of Sustainability

Zhou

2018

Sustainability

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“…The study indicated the online discussion and lecture video were more popular in the MOOCs, and 41.5% of these MOOCs had subtitles for videos in experimental environment. The experimental results illustrated the subtitles and transcription of MOOC documents were important tool for the improvement of learning efficiency [25]. Furthermore, Knowlton et al proposed and designed the web-based interactions for teaching interdisciplinary sustainability science teamwork skills to graduate students.…”

Section: Learning Of Sustainabilitymentioning

confidence: 99%

A Novel Real-Time Speech Summarizer System for the Learning of Sustainability

et al. 2015

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As the number of speech and video documents increases on the Internet and portable devices proliferate, speech summarization becomes increasingly essential. Relevant research in this domain has typically focused on broadcasts and news; however, the automatic summarization methods used in the past may not apply to other speech domains (e.g., speech in lectures). Therefore, this study explores the lecture speech domain. The features used in previous research were analyzed and suitable features were selected following experimentation; subsequently, a three-phase real-time speech summarizer for the learning of sustainability (RTSSLS) was proposed. Phase One involved selecting independent features (e.g., centrality, resemblance to the title, sentence length, term frequency, and thematic words) and calculating the independent feature scores; Phase Two involved calculating the dependent features, such as the position compared with the independent feature scores; and Phase Three involved comparing these feature scores to obtain weighted averages of the function-scores, determine the highest-scoring sentence, and OPEN ACCESSSustainability 2015, 7 3886 provide a summary. In practical results, the accuracies of macro-average and micro-average for the RTSSLS were 70% and 73%, respectively. Therefore, using a RTSSLS can enable users to acquire key speech information for the learning of sustainability.

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“…Thus, online technologies (i.e., Web 2.0 and social media) have the potential to reach wide swaths of citizenry, and may represent primary (and as of yet under-researched [16]) mediums for encouraging action to protect the environment. In fact, the utility of Web 2.0 and social media is being tested in social science research seeking to build and organize communities (e.g., [31]) and for purposes of climate change communication (e.g., [14,16,20,32]) and environmental education (e.g., [11,23,33]). However, research has yet to establish the "best" technologies and how to maximize their effectiveness.…”

Section: Introductionmentioning

confidence: 99%

“…Nonetheless, individual and collective action is needed to help preserve natural environments and mitigate environmental problems [8,9]. Web 2.0 and social media technologies are a set of communication and organizational tools that are increasingly gaining attention for their potential to heighten environmental concern and facilitate sustainable behaviors in the general public [10][11][12][13][14][15][16][17][18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Using Web 2.0 and Social Media Technologies to Foster Proenvironmental Action

2015

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Abstract:Research from a variety of disciplines suggests that online technologies (i.e., Web 2.0 and social media) have considerable potential for spurring proenvironmental action; however, relatively little work examines how to effectively capitalize on these communication and organization tools. This review paper describes the Technologies for Proenvironmental Action Model (TPAM), a conceptual framework that explicates how different functions of Web 2.0 and social media (i.e., informational, relational, and experiential) can generate and/or facilitate personal, social, and contextual pathways to environmentally responsible behaviors. As derived from the TPAM, the likelihood of achieving practical goals of increasing proenvironmental behaviors is enhanced when technological functions are matched to the different pathways to proenvironmental action. For example, the relational function of technologies, as exemplified by Social Networking Sites (SNSs), should be particularly effective in communicating social norms supportive of environmentally responsible behaviors. The TPAM is intended as a guide to develop novel approaches, research questions, and methodologies in leveraging Web 2.0 and social media technologies to promote proenvironmental action. Results will contribute to basic theory development and work in applied settings (e.g., local environmental organizations) in order to effectively communicate and organize with different segments of the population to increase sustainable behaviors. OPEN ACCESSSustainability 2015, 7 10621

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Sustainability Education in Massive Open Online Courses: A Content Analysis Approach

Cited by 56 publications

References 31 publications

Enhancing the Efficiency of Massive Online Learning by Integrating Intelligent Analysis into MOOCs with an Application to Education of Sustainability

Enhancing the Efficiency of Massive Online Learning by Integrating Intelligent Analysis into MOOCs with an Application to Education of Sustainability

A Novel Real-Time Speech Summarizer System for the Learning of Sustainability

Using Web 2.0 and Social Media Technologies to Foster Proenvironmental Action

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