Facilitating Student Success in Introductory Chemistry with Feedback in an Online Platform

Horne, Sam Van; Curran, Maura; Smith, Anna Marie; VanBuren, John M.; Zahrieh, David M.; Larsen, Russell G.; Miller, Rob

doi:10.1007/s10758-017-9341-0

Cited by 28 publications

(15 citation statements)

References 45 publications

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“…These results indicate that most LAD target outcomes refer to monitoring processes such as awareness of learning performance (Park & Jo, 2015), emotional states (Sedrakyan et al, 2017) or general awareness related to learning progress, patterns and strategies (Aljohani et al, 2019; Aljohani & Davis, 2013; Baneres et al, 2019; Bodily, 2018; Park & Jo, 2015; Seanosky et al, 2017; Sedrakyan et al, 2017). Other papers described reflection‐related target outcomes that focused on learners’ consideration of their performance (Broos, Peeters, et al, 2017; Broos, Verbert, et al, 2017; Muldner et al, 2015; Van Horne et al, 2018), behaviour and collaboration (Clayphan et al, 2017; Hill, 2018; Lkhagvasuren et al, 2016; Michel et al, 2012), learning (Cha & Park, 2019; Mejia et al, 2016; Michel et al, 2017) and reflective sense‐making (Vovides & Inman, 2016).…”

Section: Resultsmentioning

confidence: 99%

“…Based on the coded data, there is a considerable lack of alignment between LADs’ target outcomes and the domain measures used to evaluate their achievement. For example, although 20 of the LAD target outcomes (71%) related to regulation of cognition, only 6 out of these 20 LADs (30%) were actually evaluated using cognitive domain measures (eg, learning outcomes) that reflect the efficacy of the LADs relative to the stated target outcome (Aljohani et al, 2019; Kim et al, 2016; Lkhagvasuren et al, 2016; Raza et al, 2019; Seanosky et al, 2017; Van Horne et al, 2018).…”

Section: Resultsmentioning

confidence: 99%

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Staying on target: A systematic literature review on learner‐facing learning analytics dashboards

Valle

Antonenko

Dawson

et al. 2021

Brit J Educational Tech

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The advances in technology to capture and process unprecedented amounts of educational data has boosted the interest in Learning Analytics Dashboard (LAD) applications as a way to provide meaningful visual information to administrators, parents, teachers and learners. Despite the frequent argument that LADs are useful to support target users and their goals to monitor and act upon the information provided, little is known about LADs’ theoretical underpinnings and the alignment (or lack thereof) between LADs intended outcomes and the measures used to evaluate their implementation. However, this knowledge is necessary to illuminate more efficient approaches in the development and implementation of LAD tools. Guided by the self‐regulated learning perspective and using the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) framework, this systematic literature review addressed this gap by examining whether and how learner‐facing LAD’s target outcomes align with the domain measures used to evaluate their implementations. Out of the 1297 papers retrieved from 15 databases, 28 were included in the final quantitative and qualitative analysis. Results suggested an intriguing lack of alignment between LADs’ intended outcomes (mostly cognitive domain) and their evaluation (mostly affective measures). Based on these results and on the premise that LADs are designed to support learners, a critical recommendation from this study is that LADs’ target outcomes should guide the selection of measures used to evaluate the efficacy of these tools. This alignment is critical to enable the construction of more robust guidelines to inform future endeavours in the field. What is already known about this topic There has been an increased interest and investment in learning analytics dashboards to support learners as end‐users. Learner‐facing learning analytics dashboards are designed with different purposes, functionalities and types of data in an attempt to influence learners’ behaviour, achievement and skills. What this paper adds This paper reports trends and opportunities regarding the design of learner‐facing learning analytics dashboards, contexts of implementation, as well as types and features of learner‐facing learning analytics dashboard studies. The paper discusses how affect and motivation have been largely overlooked as target outcomes in learner‐facing learning analytics dashboards. Implications for practice and/or policy Based on the evidence gathered through the review, this paper makes recommendations for theory (eg, inclusion of motivation as an important target outcome). The paper makes recommendations related to the design, implementation and evaluation of learning analytics dashboards. The paper also highlights the need for further integration between learner‐facing learning analytics dashboards and open learner models.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Staying on target: A systematic literature review on learner‐facing learning analytics dashboards

Valle

Antonenko

Dawson

et al. 2021

Brit J Educational Tech

View full text Add to dashboard Cite

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“…Accurate learning strategies that aid student autonomy and adequate feedback for students to regulate their training have a positive impact on academic progress [16,17]. Arriaga, Burillo, Carpeño, and Casaravilla [18], state that students who are committed to study tasks and perceive that the support by the teaching staff is based on their requirements, usually have better academic results.…”

Section: Motivational and Self-regulated Socio-cognitive Skillsmentioning

confidence: 99%

Academic Achievement and Failure in University Studies: Motivational and Emotional Factors

et al. 2020

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Universities are committed to offering quality education; however, a high rate of academic failure is often observed in the first year of studies. Considering the impact that motivation and emotional aspects can have on students’ commitment to study and therefore on their academic performance, achievement, and well-being, this study aims to identify the factors associated with academic success or failure in 1071 students entering the National Polytechnic School (Quito, Ecuador). The data were compiled from the existing computer records of the university with the permission of the responsible administrative staff. A predictive model has been used and a binary logistic regression analysis was carried out through the step-forward regression procedure based on the Wald statistic to analyze the predictive capacity of the variables related to emotional intelligence, motivational and self- regulated socio-cognitive skills, goal orientation, and prior academic achievement (measured by university entrance marks and through a knowledge test carried out at the beginning of the university academic year). To determine the cut-off point for the best discriminatory power of each of the variables, a Receiver Operating Characteristics (ROC) curve analysis has been used. The results indicate that the variables that are significant in the prediction of academic success or failure are the two academic performance measures: the emotional attention variable, and the performance-approach goals and the motivational self-efficacy variable. Additionally, the highest predictive power is displayed by the prior academic performance measure obtained through the knowledge test conducted at the beginning of the university course.

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“…LADs are intended to provide near-real-time feedback to the student and other stakeholders (Few, 2006). Recently, researchers at the University of Iowa revealed that students who frequently monitored their LAD had significantly higher grades on assignments and tests than students who did not (Van Horne et al, 2018). However, in their literature review, Bodily and Verbert (2017) asserted that minimal research has addressed the ways students are using LADs and how to increase student use.…”

Section: Learning Analyticsmentioning

confidence: 99%

Characteristics of Students Who Opted In to Use the Boost Mobile App as an Educational Support Service

Andrews¹,

Motz²,

Israel³

et al. 2020

JoTLT

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This exploratory study aimed to investigate the characteristics of students who opted-in to use Boost, an automated student support mobile app and compare characteristics of non-Boost users. Boost is a mobile app that integrates with the Learning Management System (Canvas) and provides support services aimed at improving student behavior and success. At the start of the Spring 2019 semester at Indiana University, instructors were invited to opt-in for Boost to be available to their classes. Instructors who opted-in invited their students to use Boost. Our multivariate analysis of variance (MANOVA) compared those who opted-in for automated support with those who did not (n=158 courses). Findings reveal that opt-ins were more likely to be Asian, International, and despite having higher college entrance exam scores and being farther along in their studies, opt-ins were slightly lower performing than their peers who did not opt-in. A profile of Boost users will help university administration, student support services, and instructors make data-informed decisions on optimal use of Boost.

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Facilitating Student Success in Introductory Chemistry with Feedback in an Online Platform

Cited by 28 publications

References 45 publications

Staying on target: A systematic literature review on learner‐facing learning analytics dashboards

Staying on target: A systematic literature review on learner‐facing learning analytics dashboards

Academic Achievement and Failure in University Studies: Motivational and Emotional Factors

Characteristics of Students Who Opted In to Use the Boost Mobile App as an Educational Support Service

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