Integrating analytics and surveys to understand fully engaged learners in a highly-technical STEM MOOC

Hicks, Nathan M.; Roy, Doipayan; Shah, Siddharth; Douglas, Kerrie A.; Bermel, Peter; Diefes‐Dux, Heidi A.; Madhavan, Krishna

doi:10.1109/fie.2016.7757735

Cited by 10 publications

(7 citation statements)

References 12 publications

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“…Massive Open Online Courses (MOOCs) is an alternative distance learning paradigm, where learning activities exclusively rely on remote services and typically require realtime interaction between teachers and learners [12]. MOOCs are often open access with unlimited participation to foster learning initiatives in developing countries [13] [14] [15]. Unlike traditional MOOCs, the video service considered in this study (i) is offered to a limited number of learners with similar background knowledge, and (ii) requires no real-time student engagement as in active learning [16], since it is not aimed at substituting classroom activities.…”

Section: Related Workmentioning

confidence: 99%

Educational video services in universities: A systematic effectiveness analysis

Cagliero

Farinetti

Mezzalama

et al. 2017

2017 IEEE Frontiers in Education Conference (FIE)

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Our university has offered a massive educational video service since 2010, as part of a blended learning model that allows students to balance active participation in the classroom with remote access to video-recorded lectures. In these years, we have collected a huge amount of very detailed data about the students' access to the service. Together with additional information that characterize a university system (e.g. students' performance or course population), these data represent a precious ground set to assess the educational model. The paper describes an experimental set to profile the use of the educational video service, whose results will contribute to improve the model. Specifically the paper analyzes the students' service use relatively to different transversal course characteristics, such as level, main topic, population, success rate. As a result, it outlines the profile of the "ideal" courses for which students highly appreciate the service. This information will help educational designers to select the future courses to be included in the service, but it will also give directions on the sectors where improvements are necessary. Finally, the paper experimentally demonstrates a positive impact of the educational video service on students' performance, and specifically on the exam success rate.

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Section: Related Workmentioning

confidence: 99%

Educational video services in universities: A systematic effectiveness analysis

Cagliero

Farinetti

Mezzalama

et al. 2017

2017 IEEE Frontiers in Education Conference (FIE)

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“…2) Identifying learner groups-We identify and label four categories of learners that emerge from access-based clustering using the K-means algorithm. These clusters are very similar to the ones observed by Douglas, et al [20] and Hicks, et al [21]. Thus, we use similar nomenclature for identifying the clusters:…”

Section: Figure 1-a Typical Elbow Plotsupporting

confidence: 82%

“…In this paper, we define behavior as the pattern in which a learner accesses resources available on the learning platform. Douglas, et al [20] and Hicks, et al [21] characterized learners based on usage patterns using K-means clustering. Hicks, et al [21] focused almost entirely on Fully Engaged Learners and integrated survey data collected before the start of the course to compare the motivation and behavior of this group with other groups.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Douglas, et al [20] and Hicks, et al [21] characterized learners based on usage patterns using K-means clustering. Hicks, et al [21] focused almost entirely on Fully Engaged Learners and integrated survey data collected before the start of the course to compare the motivation and behavior of this group with other groups. In contrast, Douglas, et al [20] investigated the behavior of all learner groups in detail and used the Kruskal-Wallis test to show that the differences in performance (as measured by assessment scores) and participation across clusters was statistically significant.…”

Section: Literature Reviewmentioning

confidence: 99%

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Synthesis of clustering techniques in educational data mining

Roy¹,

Bermel²,

Douglas³

et al.

2017 ASEE Annual Conference &Amp; Exposition Proceedings

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His research focuses on improving the performance of photovoltaic, thermophotovoltaic, and nonlinear systems using the principles of nanophotonics. Key enabling techniques for his work include electromagnetic and electronic theory, modeling, simulation, fabrication, and characterization. Dr. Bermel is widely-published in both scientific peer-reviewed journals and publications geared towards the general public. His work, which has been cited over 4400 times, for an h-index value of 24, includes the following topics: * Understanding and optimizing the detailed mechanisms of light trapping in thinfilm photovoltaics * Fabricating and characterizing 3D inverse opal photonic crystals made from silicon for photovoltaics, and comparing to theoretical predictions * Explaining key physical effects influencing selective thermal emitters in order to achieve high performance thermophotovoltaic systems

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“…Engaging students in MOOCs environment especially for non-technical subjects, suit very well. However, for technical MOOCs, it involved significant challenges because technical MOOCs must able to offer practice-oriented learning in order for the MOOCs to be effective and engaging [5], [13], [22], [23]. In this context, few researchers suggested the inclusion of remote laboratory [5], [13] and wearable technology [24], [25] as an alternative to engaging the students more with the MOOCs.…”

Section: Introductionmentioning

confidence: 99%

Wearable Technology in Education to Enhance Technical MOOCs

Fesol

Salam

Bakar

2018

Int. J. Adv. Sci. Eng. Inf. Technol.

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The low completion rate issue in MOOCs has become one of the main highlights by researchers. It is reported that only 10 to 15 percent of the students were able to complete the MOOCs. This low completion rate is due to the students are less engaged with the MOOCs content causing them to become demotivated to complete the whole MOOCs. Engaging students in MOOCs environment, especially for non-technical subjects, suited very well. However, for technical MOOCs, it involves significant challenges because technical MOOCs must be able to offer the students with practice-oriented learning outcome in-order for the MOOCs to be effective and engaging. Due to the above issue, this paper discussed an improvised MOOCs model for teaching technical subjects with the intervention in the use of wearable technology. A discussion on the e-content and e-activity of the technical MOOC learning design was presented. This study implemented the proposed technical MOOC model through the MOOC development. The students' perception of the technical MOOC was then evaluated, and all the results were explained in the discussion part. Results of the correlation test revealed that all of the technical MOOC variables correlated substantially and held a positive relationship with students' perception variable with r=.3 to 1.0, p < .0005. In addition, this result also suggests that the learning materials produced by wearable technology do contribute towards the positive effect of students' perception when learning technical MOOC.

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Integrating analytics and surveys to understand fully engaged learners in a highly-technical STEM MOOC

Cited by 10 publications

References 12 publications

Educational video services in universities: A systematic effectiveness analysis

Educational video services in universities: A systematic effectiveness analysis

Synthesis of clustering techniques in educational data mining

Wearable Technology in Education to Enhance Technical MOOCs

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