Widget, Widget on the Wall, Am I Performing Well at All?

Scheffel, Maren; Drachsler, Hendrik; Kraker, Joop de; Kreijns, Karel; Slootmaker, Aad; Specht, Marcus

doi:10.1109/tlt.2016.2622268

Cited by 22 publications

(17 citation statements)

References 29 publications

(31 reference statements)

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“…In this study, we examined whether and how the degree of OUA usage is related to student's performance (RQ1) and whether the OUA usage as compared to non‐PLA‐related student's support approaches results in better learning outcomes (RQ2). In terms of RQ1, this study suggests that frequent access to OUA by online teachers can enhance student's performance and increase pass rates (Herodotou et al , n.d.; Scheffel et al , ). “Average” OUA usage (as low as 10% of the weeks a course runs, yet spread across the life cycle of a course presentation, see Figure ) can have a positive impact on student's outcomes.…”

Section: Discussionmentioning

confidence: 99%

“…A wide body of literature suggests that predictive learning analytics (PLA) can help teachers and institutions in general, to overcome such challenges, by identifying which groups of students might need extra support to reach desired learning outcomes (Conijn, Snijders, Kleingeld, & Matzat, ; Haya et al , ; Herodotou et al , ; Kovanović et al , ; Scheffel et al , ). For example, Scheffel et al () found that visualizing the relative performance of 172 students using the PLA supported the learning processes over time. Yet, Conijn et al () found that the PLA results varied substantially and concluded that virtual learning environment's (VLE's) engagement and clicking data are of limited value in the effective prediction of learning outcomes.…”

Section: Introductionmentioning

confidence: 99%

“…British Journal of Educational Technology Vol 50 No 6 2019& Matzat, 2017Haya et al, 2015;Herodotou et al, 2017;Kovanović et al, 2015;Scheffel et al, 2017). For example, Scheffel et al (2017) found that visualizing the relative performance of 172 students using the PLA supported the learning processes over time. Yet, Conijn et al (2017) found that the PLA results varied substantially and concluded that virtual learning environment's (VLE's) engagement and clicking data are of limited value in the effective prediction of learning outcomes.…”

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confidence: 99%

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Empowering online teachers through predictive learning analytics

Herodotou¹,

Hlosta²,

Boroowa³

et al. 2019

Brit J Educational Tech

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This study presents an advanced predictive learning analytics system, OU Analyse (OUA), and evidence from its evaluation with online teachers at a distance learning university. OUA is a predictive system that uses machine learning methods for the early identification of students at risk of not submitting (or failing) their next assignment. Teachers have access, via interactive dashboards, to weekly predictions of risk of failing for each of their students. In this study, we examined how the degree of OUA usage by 559 teachers, of which 189 were given access to OUA, related to student learning outcomes of more than 14 000 students in 15 undergraduate courses. Teachers who made “average” use of OUA, that is accessed OUA throughout the life cycle of a course presentation, and in particular between 10% and 40% of the weeks a course was running, and intervened with students flagged as at risk were found to benefit their students the most; after controlling for differences in academic performance, these students were found to have significantly better performance than their peers in the previous year's course presentation during which the same teachers made no use of predictive learning analytics. Predictive learning analytics is an innovative student's support approach in online pedagogy that, as shown in this study, can empower online teachers in effectively monitoring and intervening with their students, over and above other approaches, and result in improved learning outcomes. What is already known about this topic Pedagogical and personal support to students is a significant responsibility of online teachers. Student's support is a challenging activity due to the lack of face‐to‐face interactions. Predictive learning analytics (PLA) can identify students at risk of failing their studies. What this paper adds One of the few large‐scale studies is available for examining the impact of analytics on student's performance. Teachers' usage of PLA was significantly related to better learning outcomes. Online teachers had students with better learning outcomes when accessing PLA data rather than when they had no access. Implications for practice and/or policy PLA can empower online teachers and complement the teaching practice. PLA can help in the identification and proactive intervention of students at risk of failing their studies. Actions should be taken to motivate and engage online teachers with PLA.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Empowering online teachers through predictive learning analytics

Herodotou¹,

Hlosta²,

Boroowa³

et al. 2019

Brit J Educational Tech

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“…Because total behavior frequency has often been used to represent behavioral engagement level [54]- [56], and many researchers have claimed that high behavioral engagement level has positive correlations with high learning performance [54], [57], it is not surprised that if a student seldom accesses to the online learning system, he/she is unlikely to perform well. In addition, the 'Discussion_word_counts' variable is a general signal about a student's discussion engagement level [58].…”

Section: ) Identification Of Significant Predictorsmentioning

confidence: 99%

An Integrated Framework Based on Latent Variational Autoencoder for Providing Early Warning of At-Risk Students

Yang

Hung

2020

IEEE Access

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The rapid development of learning technologies has enabled online learning paradigm to gain great popularity in both high education and K-12, which makes the prediction of student performance become one of the most popular research topics in education. However, the traditional prediction algorithms are originally designed for balanced dataset, while the educational dataset typically belongs to highly imbalanced dataset, which makes it more difficult to accurately identify the at-risk students. In order to solve this dilemma, this study proposes an integrated framework (LVAEPre) based on latent variational autoencoder (LVAE) with deep neural network (DNN) to alleviate the imbalanced distribution of educational dataset and further to provide early warning of at-risk students. Specifically, with the characteristics of educational data in mind, LVAE mainly aims to learn latent distribution of at-risk students and to generate at-risk samples for the purpose of obtaining a balanced dataset. DNN is to perform final performance prediction. Extensive experiments based on the collected K-12 dataset show that LVAEPre can effectively handle the imbalanced education dataset and provide much better and more stable prediction results than baseline methods in terms of accuracy and F 1.5 score. The comparison of t-SNE visualization results further confirms the advantage of LVAE in dealing with imbalanced issue in educational dataset. Finally, through the identification of the significant predictors of LVAEPre in the experimental dataset, some suggestions for designing pedagogical interventions are put forward. INDEX TERMS Performance prediction, early warning prediction, latent variational autoencoder, resampling methods, deep neural network, t-SNE.

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“…Beyond providing markers for formative feedback on cognitive skills (e.g., mastery of mathematics, critical thinking), formative analytics tools have also been used for more twenty-first century affective (e.g., anxiety, self-efficacy) and behavioral (e.g., group working) skills. For example, a group widget developed by Scheffel et al (2017) showed that group members were more aware of their online peers and their contributions. Similarly, providing automatic computer-based assessment feedback on mastery of mathematics exercises but also providing different options to work-out the next task allowed students with math anxiety to develop more self-efficacy over time when they actively engaged with formative analytics (Tempelaar et al, 2018).…”

Section: Relation To the Development Of Twenty-first Century Skillsmentioning

confidence: 99%

Innovative Pedagogies of the Future: An Evidence-Based Selection

et al. 2019

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There is a widespread notion that educational systems should empower learners with skills and competences to cope with a constantly changing landscape. Reference is often made to skills such as critical thinking, problem solving, collaborative skills, innovation, digital literacy, and adaptability. What is negotiable is how best to achieve the development of those skills, in particular which teaching and learning approaches are suitable for facilitating or enabling complex skills development. In this paper, we build on our previous work of exploring new forms of pedagogy for an interactive world, as documented in our Innovating Pedagogy report series. We present a set of innovative pedagogical approaches that have the potential to guide teaching and transform learning. An integrated framework has been developed to select pedagogies for inclusion in this paper, consisting of the following five dimensions: (a) relevance to effective educational theories, (b) research evidence about the effectiveness of the proposed pedagogies, (c) relation to the development of twenty-first century skills, (d) innovative aspects of pedagogy, and (e) level of adoption in educational practice. The selected pedagogies, namely formative analytics, teachback, place-based learning, learning with drones, learning with robots, and citizen inquiry are either attached to specific technological developments, or they have emerged due to an advanced understanding of the science of learning. Each one is presented in terms of the five dimensions of the framework.

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Widget, Widget on the Wall, Am I Performing Well at All?

Cited by 22 publications

References 29 publications

Empowering online teachers through predictive learning analytics

Empowering online teachers through predictive learning analytics

An Integrated Framework Based on Latent Variational Autoencoder for Providing Early Warning of At-Risk Students

Innovative Pedagogies of the Future: An Evidence-Based Selection

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