Tracking and Visualising Student Effort: Evolution of a Practical Analytics Tool for Staff and Student Engagement

Nagy, Robin P.

doi:10.18608/jla.2016.32.8

Cited by 19 publications

(8 citation statements)

References 9 publications

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“…To answer RQ1, we aggregated the results of disengaged and partially disengaged (Red + Yellow) to identify the relation between student performance in FAs, engagement and final programming exam results. Figure 2 presents the results of disengaged students (Red + Yellow) that failed in FE in the period 2016. Fig.…”

Section: Data Analysis and Resultsmentioning

confidence: 99%

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Formative assessment tasks as indicators of student engagement for predicting at-risk students in programming courses

Veerasamy¹,

Laakso²,

D'Souza³

2021

Informatics in Education

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Previous studies have proposed many indicators to assess the effect of student engagement in learning and academic achievement but have not yet been clearly articulated. In addition, while student engagement tracking systems have been designed, they rely on the log data but not on performance data. This paper presents results of a non-machine learning model developed using ongoing formative assessment scores as indicators of student engagement. Visualisation of the classification tree results is employed as student engagement indicators for instructors to observe and intervene with students. The results of this study showed that ongoing assessment is related to student engagement and subsequent final programming exam performance and possible to identify students at-risk of failing the final exam. Finally, our study identified students impacted by the Covid-19 pandemic. These were students who attended the final programming exam in the semester 2019-2020 and who scored well in formative assessments. Based on these results we present a simple student engagement indicator and its potential application as a student progress monitor for early identification of students at risk.

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Section: Data Analysis and Resultsmentioning

confidence: 99%

“…Several studies have been carried out to determine the factors for measuring student engagement (Bond, 2020;Jeongju Lee, 2019). In addition, student progress monitoring systems have been developed to use such indicators for assessing the effect of engagement in student performance (Nagy, 2016;Henrie;R. Halverson;& Graham, 2015).…”

Section: Introductionmentioning

confidence: 99%

Formative assessment tasks as indicators of student engagement for predicting at-risk students in programming courses

Veerasamy¹,

Laakso²,

D'Souza³

2021

Informatics in Education

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“…Relative to this assertion, EFL teaching processes have been affected by concerns regarding the methodologies chosen to assess learning outcomes (Huang, Teo, & Zhou, 2019). For example, some researchers have expressed concern regarding the overreliance on academic rewards mechanism to improve educational outcomes because there is enough evidence to suggest that using academic success as the main metric of evaluation does not necessarily improve educational outcomes (Fischer et al, 2016;Nagy, 2016). To address this problem, educational stakeholders need to make a concerted effort to provide a holistic framework for evaluating educational outcomes.…”

Section: Improving Outcomes Of Efl Teaching and Learning Through Information Systemsmentioning

confidence: 99%

Data-Informed Educational Decision Making to Improve Teaching and Learning Outcomes of EFL

Aburizaizah¹

2021

JEL

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For many justifications, the collection, analysis, and use of educational data are central to the evaluation and improvement of students’ progress and learning outcomes. The use of data in educational evaluation and decision making are expected to span all layers—from the institution, teachers, students, and classroom levels, providing a longitudinal record of each student’s performance over time. Such records/data can play a crucial role by giving students, teachers, parents, and stakeholders a scalable and efficient platform that track performance and lead to informed valid enhancement decisions. This paper provides a description of a proposed tracking system. Developed by an English Language institute. It has multiple key features and processes that can monitor the progress of students from day 1 till completing their study. It is a comprehensive integration of student data management and a monitoring system. Such data makes it possible to see if students are achieving their academic goals and administrator could see, as soon as possible, if a student is not progressing. The system is also useful in helping the institute to plan their educational activities every semester and improve data communication between administrator, teachers, and students.

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“…For instance, with the aim of developing high-level epistemic agency and design mode thinking competencies, Chen and Zhang (2016) face education as a knowledge-creation enterprise, where LA is inspired in a conceptual model of knowledge-building analytics that embraces agency-driven, choice-based, and progress-oriented analytics. Moreover, qualities that lifelong learners need to succeed (e.g., effort revealed by behavior, diligence, engagement, and persistence) are evaluated by means of visual LA that facilitates a productive dialogue among staff and students (Nagy, 2016).…”

Section: Internalizationmentioning

confidence: 99%

Learning analytics: A glance of evolution, status, and trends according to a proposed taxonomy

Peña-Ayala

2018

WIREs Data Min & Knowl

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Before the emergence of computer‐based educational systems (CBES) whose aims of providing teaching and learning experiences to hundreds even thousands of users, an explosion of information (e.g., students' log data) demands sophisticated methods to gather, analyze, and interpret learners' traces to regulate and enhance education. Thus, learning analytics (LA) arises as a knowledge discovery paradigm that provides valuable findings and facilitates stakeholders to understand the learning process and its implications. Therefore, a landscape of the LA nature, its underlying factors, and applications achieved is outlined in this paper according to a suggested LA Taxonomy that classifies the LA duty from a functional perspective. The aim is to provide an idea of the LA toil, its research lines, and trends to inspire the development of novel approaches for improving teaching and learning practices. Furthermore, the scope of this review covers recently published papers in prestigious journals and conferences, where the works dated from 2016 are summarized and those corresponding to 2014–2015 are cited according to the proposed LA taxonomy. A glimpse is sketched of LA, where underlying elements frame the field foundations to ground the approaches. Moreover, LA strengths, weaknesses, challenges, and risks are highlighted to advice how the LA arena could be enhanced and empowered. In addition, this review offers an insight of the recent LA labor, as well as motivates readers to enrich the LA achievements. This work promotes the LA practice giving an account of the job being achieved and reported in literature, as well as a reflection of the state‐of‐the‐art and an acumens vision to inspire future labor. This article is categorized under: Application Areas > Education and Learning Application Areas > Science and Technology Fundamental Concepts of Data and Knowledge > Human Centricity and User Interaction

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Tracking and Visualising Student Effort: Evolution of a Practical Analytics Tool for Staff and Student Engagement

Cited by 19 publications

References 9 publications

Formative assessment tasks as indicators of student engagement for predicting at-risk students in programming courses

Formative assessment tasks as indicators of student engagement for predicting at-risk students in programming courses

Data-Informed Educational Decision Making to Improve Teaching and Learning Outcomes of EFL

Learning analytics: A glance of evolution, status, and trends according to a proposed taxonomy

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