Communication at Scale in a MOOC Using Predictive Engagement Analytics

Le, Christopher Vu; Pardos, Zachary A.; Meyer, Samuel D.; Thorp, Rachel

doi:10.1007/978-3-319-93843-1_18

Cited by 18 publications

(15 citation statements)

References 15 publications

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“…Big data are also used to understand the effectiveness of administrative decisions and educational interventions. Big data models can predict when actions need to be taken for students, such as identifying when students are disengaging from online courses (Le et al, 2018). For instance, Whitehill et al (2015) analyzed more than 2 million data points generated by more than 200,000 students taking 10 MOOC courses from HarvardX to develop detectors of whether a student would stop course work.…”

Section: Microlevel Big Datamentioning

confidence: 99%

Mining Big Data in Education: Affordances and Challenges

Fischer

Pardos

Baker

et al. 2020

Review of Research in Education

Self Cite

252

138

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The emergence of big data in educational contexts has led to new data-driven approaches to support informed decision making and efforts to improve educational effectiveness. Digital traces of student behavior promise more scalable and finer-grained understanding and support of learning processes, which were previously too costly to obtain with traditional data sources and methodologies. This synthetic review describes the affordances and applications of microlevel (e.g., clickstream data), mesolevel (e.g., text data), and macrolevel (e.g., institutional data) big data. For instance, clickstream data are often used to operationalize and understand knowledge, cognitive strategies, and behavioral processes in order to personalize and enhance instruction and learning. Corpora of student writing are often analyzed with natural language processing techniques to relate linguistic features to cognitive, social, behavioral, and affective processes. Institutional data are often used to improve student and administrational decision making through course guidance systems and early-warning systems. Furthermore, this chapter outlines current challenges of accessing, analyzing, and using big data. Such challenges include balancing data privacy and protection with data sharing and research, training researchers in educational data science methodologies, and navigating the tensions between explanation and prediction. We argue that addressing these challenges is worthwhile given the potential benefits of mining big data in education.

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Section: Microlevel Big Datamentioning

confidence: 99%

Mining Big Data in Education: Affordances and Challenges

Fischer

Pardos

Baker

et al. 2020

Review of Research in Education

Self Cite

252

138

View full text Add to dashboard Cite

show abstract

“…From a more general perspective, DeepLMS aligns with the previous efforts that incorporate LSTM-based predictions in the context of online education, yet not at the exact same specific problem settings as in DeepLMS. Hence, the latter is well-positioned with the approaches related to: i) cross-domains analysis, e.g., MOOCs impact in different contexts 57 , as DeepLMS could be easily adapted to a micro analysis of the QoI per discipline/course and transfer learning from one discipline to another at the same course (or courses with comparable content), as shown here with the application of DeepLMS to DB1-DB3, in a similar manner that was applied in MOOCs from different domains 57 ; ii) combination of learning patterns in the context domain with the temporal nature of the clickstream data 58 , and identification of students at risk 59 , as DeepLMS could be combined with an autoencoder to capture both the underlying behavioral patterns and the temporal nature of the interaction data at various levels of the predicted QoI (e.g., low (<0.5) QoI (at risk level)); iii) predicting learning gains by incorporating skills discovery 60,61 , as DeepLMS could provide the predicted QoI as an additional source of the user profile to his/her skills and learning gains; iv) user learning states and learning activities prediction from wearable devices 62 , as DeepLMS could easily be embedded in the expanded space of affective (a-) learning, and inform a more extended predictive model that would incorporate the learning state with the estimated QoI; v) increasing the communication of the instructional staff to learners based on individual predictions of their engagement during MOOCs 63,64 , as DeepLMS could facilitate the coordination of the instructor with the learner based on the informed predicted QoI; and vi) predicting the learning paths/performance 65 and the teaching paths 66 , as the DeepLMS could be extended in the context of affecting the learning/teaching path by the predicted QoI.…”

Section: Discussionmentioning

confidence: 99%

DeepLMS: a deep learning predictive model for supporting online learning in the Covid-19 era

Dias

Hadjileontiadou

Diniz

et al. 2020

Sci Rep

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Coronavirus (Covid-19) pandemic has imposed a complete shut-down of face-to-face teaching to universities and schools, forcing a crash course for online learning plans and technology for students and faculty. In the midst of this unprecedented crisis, video conferencing platforms (e.g., Zoom, WebEx, MS Teams) and learning management systems (LMSs), like Moodle, Blackboard and Google Classroom, are being adopted and heavily used as online learning environments (OLEs). However, as such media solely provide the platform for e-interaction, effective methods that can be used to predict the learner’s behavior in the OLEs, which should be available as supportive tools to educators and metacognitive triggers to learners. Here we show, for the first time, that Deep Learning techniques can be used to handle LMS users’ interaction data and form a novel predictive model, namely DeepLMS, that can forecast the quality of interaction (QoI) with LMS. Using Long Short-Term Memory (LSTM) networks, DeepLMS results in average testing Root Mean Square Error (RMSE) $$<0.009$$ < 0.009 , and average correlation coefficient between ground truth and predicted QoI values $$r\ge 0.97$$ r ≥ 0.97 $$(p<0.05)$$ ( p < 0.05 ) , when tested on QoI data from one database pre- and two ones during-Covid-19 pandemic. DeepLMS personalized QoI forecasting scaffolds user’s online learning engagement and provides educators with an evaluation path, additionally to the content-related assessment, enriching the overall view on the learners’ motivation and participation in the learning process.

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“…From these data streams, a wide range of higher level features can be inferred including affect, attention, cognitive processing, stress and fatigue. Thus, MMLA may be applicable in a wide range of educational contexts including face-to-face interactions without technological aids (Di Mitri et al, 2017;Pijeira-Díaz, Drachsler, Kirschner, & Järvelä, 2018;Spikol et al, 2018), face-to-face technology enhanced learning (Liu et al, 2016;Viswanathan & VanLehn, 2017b) and online learning (Le, Pardos, Meyer, & Thorp, 2018). Within MMLA, many different combinations of data streams have been explored but it is not clear what makes the different combinations impactful for collaborative learning.…”

Section: Practitioner Notesmentioning

confidence: 99%

“…When using LSTMs with outcome data, they can be used to find successful and unsuccessful strategies (Akram et al, 2018), predict the quality of a presentation (Li, Wong, & Kankanhalli, 2016) or predict learning outcomes (Okubo, Yamashita, Shimada, & Ogata, 2017). LSTMs can more accurately predict learning than common methods like Bayesian knowledge tracing (Piech et al, 2015) On the other hand, they have also been used to provide interventions during the learning process by providing learning recommendations (Zhou, Huang, Hu, Zhu, & Tang, 2018), predicting engagement and affect (Botelho, Baker, & Heffernan, 2017;Le et al, 2018), collaboration actions (Shibata, Ando, & Inaba, 2017), learning state (Wang, Sy, Liu, & Piech, 2017) and forum post relationships (Wei, Lin, Yang, & Yu, 2017). The longer history afforded through the use of LSTMs provides a way for the predictions to consider temporal patterns.…”

Section: Temporal Analysis In Educationmentioning

confidence: 99%

Temporal analysis of multimodal data to predict collaborative learning outcomes

Olsen¹,

Sharma²,

Rummel³

et al. 2020

Brit J Educational Tech

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The analysis of multiple data streams is a long‐standing practice within educational research. Both multimodal data analysis and temporal analysis have been applied successfully, but in the area of collaborative learning, very few studies have investigated specific advantages of multiple modalities versus a single modality, especially combined with temporal analysis. In this paper, we investigate how both the use of multimodal data and moving from averages and counts to temporal aspects in a collaborative setting provides a better prediction of learning gains. To address these questions, we analyze multimodal data collected from 25 9–11‐year‐old dyads using a fractions intelligent tutoring system. Assessing the relation of dual gaze, tutor log, audio and dialog data to students' learning gains, we find that a combination of modalities, especially those at a smaller time scale, such as gaze and audio, provides a more accurate prediction of learning gains than models with a single modality. Our work contributes to the understanding of how analyzing multimodal data in temporal manner provides additional information around the collaborative learning process.

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Communication at Scale in a MOOC Using Predictive Engagement Analytics

Cited by 18 publications

References 15 publications

Mining Big Data in Education: Affordances and Challenges

Mining Big Data in Education: Affordances and Challenges

DeepLMS: a deep learning predictive model for supporting online learning in the Covid-19 era

Temporal analysis of multimodal data to predict collaborative learning outcomes

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