Learning Analytics to Inform and Guide Teachers as Designers of Educational Interventions

Aruvee, Merilin; Ljalikova, Aleksandra; Vahter, Edna; Prieto, Luis P.; Poom-Valickis, Katrin

doi:10.21125/edulearn.2018.0666

Cited by 3 publications

(3 citation statements)

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“…By selecting the student ID from the checkbox list, the performance levels are shown (Figure 4a). To define the percentage of performance, we apply Equation (2) where 𝑁 and 𝑁 are the numbers of correct and incorrect answers, respectively, for each student. Performance 100,…”

Section: Overall Design Of Sbgtool V20mentioning

confidence: 99%

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SBGTool v2.0: An Empirical Study on a Similarity-Based Grouping Tool for Students’ Learning Outcomes

Mohseni

Martins

Masiello

2022

Data

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Visual learning analytics (VLA) tools and technologies enable the meaningful exchange of information between educational data and teachers. This allows teachers to create meaningful groups of students based on possible collaboration and productive discussions. VLA tools also allow a better understanding of students’ educational demands. Finding similar samples in huge educational datasets, however, involves the use of effective similarity measures that represent the teacher’s purpose. In this study, we conducted a user study and improved our web-based similarity-based grouping VLA tool, (SBGTool) to help teachers categorize students into groups based on their similar learning outcomes and activities. SBGTool v2.0 differs from SBGTool due to design changes made in response to teacher suggestions, the addition of sorting options to the dashboard table, the addition of a dropdown component to group the students into classrooms, and improvement in some visualizations. To counteract color blindness, we have also considered a number of color palettes. By applying SBGTool v2.0, teachers may compare the outcomes of individual students inside a classroom, determine which subjects are the most and least difficult over the period of a week or an academic year, identify the numbers of correct and incorrect responses for the most difficult and easiest subjects, categorize students into various groups based on their learning outcomes, discover the week with the most interactions for examining students’ engagement, and find the relationship between students’ activity and study success. We used 10,000 random samples from the EdNet dataset, a large-scale hierarchical educational dataset consisting of student–system interactions from multiple platforms at the university level, collected over a two-year period, to illustrate the tool’s efficacy. Finally, we provide the outcomes of the user study that evaluated the tool’s effectiveness. The results revealed that even with limited training, the participants were able to complete the required analysis tasks. Additionally, the participants’ feedback showed that the SBGTool v2.0 gained a good level of support for the given tasks, and it had the potential to assist teachers in enhancing collaborative learning in their classrooms.

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Section: Overall Design Of Sbgtool V20mentioning

confidence: 99%

“…Learning analytics (LA) has evolved to help in the interpretation of educational data [2]. Siemens et al [3] (p. 1) define LA as "the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs".…”

Section: Introductionmentioning

confidence: 99%

SBGTool v2.0: An Empirical Study on a Similarity-Based Grouping Tool for Students’ Learning Outcomes

Mohseni

Martins

Masiello

2022

Data

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“…Because each student may engage in a variety of activities through the DLM over a long period of time, datasets that document such activities are typically high-dimensional, large, and complex. Learning Analytics (LA) was established to aid in the interpretation of educational data [1]. According to Siemens et al [2] (p.1), LA is "the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs".…”

Section: Introductionmentioning

confidence: 99%

Co-Developing an Easy-to-Use Learning Analytics Dashboard for Teachers in Primary/Secondary Education: A Human-Centered Design Approach

Mohseni,

Masiello,

Martins

2023

Education Sciences

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Learning Analytics Dashboards (LADs) can help provide insights and inform pedagogical decisions by supporting the analysis of large amounts of educational data, obtained from sources such as Digital Learning Materials (DLMs). Extracting requirements is a crucial step in developing a LAD, as it helps identify the underlying design problem that needs to be addressed. In fact, determining the problem that requires a solution is one of the primary objectives of requirements extraction. Although there have been studies on the development of LADs for K12 education, these studies have not specifically emphasized the use of a Human-Centered Design (HCD) approach to better comprehend the teachers’ requirements and produce more stimulating insights. In this paper we apply prototyping, which is widely acknowledged as a successful way for rapidly implementing cost-effective designs and efficiently gathering stakeholder feedback, to elicit such requirements. We present a three-step HCD approach, involving a design cycle that employs paper and interactive prototypes to guide the systematic and effective design of LADs that truly meet teacher requirements in primary/secondary education, actively engaging them in the design process. We then conducted interviews and usability testing to co-design and develop a LAD that can be used in classroom’s everyday learning activities. Our results show that the visualizations of the interactive prototype were easily interpreted by the participants, verifying our initial goal of co-developing an easy-to-use LAD.

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Development of Visual Learning Analytic Tools to Explore Performance and Engagement of Students in Primary, Secondary, and Higher Education

Mohseni

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Schools and educational institutions collect large amounts of data about students and their learning, including text, grades, quizzes, timestamps, and other activities. However, in primary and secondary education, this data is often dispersed across different digital platforms, lacking standardized methods for collection, processing, analysis, and presentation. These issues hinder teachers and students from making informed decisions or strategic and effective use of data. This presents a significant obstacle to progress in education and the effective development of Educational Technology (EdTech) products. Visual Learning Analytics (VLA) tools, also known as Learning Analytics Dashboards (LADs), are designed to visualize student data to support pedagogical decision-making. Despite their potential, the effectiveness of these tools remains limited. Addressing these challenges requires both technical solutions and thoughtful design considerations, as explored in Papers 1 through 5 of this thesis. Paper 1 examines the design aspects of VLA tools by evaluating higher education data and various visualization and Machine Learning (ML) techniques. Paper 2 provides broader insights into the VLA landscape through a systematic review, mapping key concepts and research gaps in VLA and emphasizing the potential of VLA tools to enhance pedagogical decisions and learning outcomes. Meanwhile, Paper 3 delves into a technical solution (data pipeline and data standard) considering a secure Swedish warehouse, SUNET. This includes a data standard for integrating educational data into SUNET, along with customized scripts to reformat, merge, and hash multiple student datasets. Papers 4 and 5 focus on design aspects, with Paper 4 discussing the proposed Human-Centered Design (HCD) approach involving teachers in co-designing a simple VLA tool. Paper 5 introduces a scenario-based framework for Multiple Learning Analytics Dashboards (MLADs) development, stressing user engagement for tailored LADs that facilitate informed decision-making in education. The dissertation offers a comprehensive approach to advancing VLA tools, integrating technical solutions with user-centric design principles. By addressing data integration challenges and involving users in tool development, these efforts aim to empower teachers in leveraging educational data for improved teaching and learning experiences.

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Learning Analytics to Inform and Guide Teachers as Designers of Educational Interventions

Cited by 3 publications

References 0 publications

SBGTool v2.0: An Empirical Study on a Similarity-Based Grouping Tool for Students’ Learning Outcomes

SBGTool v2.0: An Empirical Study on a Similarity-Based Grouping Tool for Students’ Learning Outcomes

Co-Developing an Easy-to-Use Learning Analytics Dashboard for Teachers in Primary/Secondary Education: A Human-Centered Design Approach

Development of Visual Learning Analytic Tools to Explore Performance and Engagement of Students in Primary, Secondary, and Higher Education

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