Visual Analysis of the Time Management of Learning Multiple Courses in Online Learning Environment

He, Huan; Dong, Bo; Di, Dehai; Lin, Yan‐Hui

doi:10.1109/visual.2019.8933778

Cited by 7 publications

(3 citation statements)

References 17 publications

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“…Ez-zaouia and Lavoué [16] presented the EMODA dashboard, which enables teachers to track learners' emotions and gain insights into how emotions change during online learning sessions. LearnerVis, developed by He et al [17] is a tool that visualizes the temporal aspects of the learning process and helps users analyze students' learning schedules across multiple courses. Mohseni et al [5,18] proposed the SBGTool and SBGTool v2.0 to assist teachers in identifying student groups with similar learning outcomes and activities, exploring the relationship between student engagement and achievement, and maximizing the potential for collaborative learning by analyzing students' responses.…”

Section: Related Work 21 Learning Analytics Dashboardsmentioning

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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Section: Related Work 21 Learning Analytics Dashboardsmentioning

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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“…In [8], Govaerts et al established the Student Activity Meter (SAM), a visualization tool for awareness and self-reflection for teachers and students to assist in evaluating how much time and Data 2022, 7, 98 3 of 18 resources students spend on learning activities. On the other hand, He et al [9] proposed LearnerVis based on their offered engagement calendar matrix model to let users view the temporal characteristics of the learning process and examine how students schedule their multicourse activities in an online learning environment. Mohseni et al [10] developed the SAVis tool by interpreting the visualization of machine learning (ML) algorithms to enable teachers to explore students' learning and activities by interacting with various visualizations of student data.…”

Section: Related Workmentioning

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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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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“…While elearning offers flexibility and convenience, it also presents certain challenges to goal setting among students. Studies have investigated issues related to time management, distractions, and self-regulation in the e-learning context (He, et al, 2019;Eggers, et al, 2021). Moreover, research has addressed potential barriers to goal attainment, such as lack of access to technology, digital literacy, and motivation (Gottschalk, et.…”

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confidence: 99%

Goal Setting, self-management skills, and evaluating ability of Chinese higher vocational college students

Xiao

2024

IJRSM

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This study explored the complex relationship among goal setting, self-management skills, and evaluating ability among Chinese higher vocational college students in the realm of e-learning. In an era marked by significant digital transformations in education, comprehending these factors is paramount for harnessing the full potential of e-learning environments. The research employed two research methods to investigate goal setting, self-management skills, and evaluating ability among Chinese higher vocational college students in e-learning contexts. A descriptive survey provided insights into population characteristics, behaviors, attitudes, and opinions. Concurrently, literature research explored contemporary findings, enriching scholarly discourse on the subject. In this study, proportional distribution of 630 questionnaires was conducted among students spanning three grades across three institutions. Subsequently, 544 valid questionnaires were collected and analyzed by SPSS software. The analysis revealed a predominantly female respondent group with fairly even distribution across various academic years and majors. Significant differences were observed in goal setting based on sex, except for mentor support, goal stress, and goal conflict. Differences were also noted in mentor support, goal stress, and goal conflict based on years of study, and across all variables of goal setting based on major.Regarding self-management skills, differences were evident in time management based on sex; selfregulated learning (goal-related) based on years of study; and self-regulated learning (goal-related), selfmotivation, and self-discipline based on major. Evaluating ability showed significant differences based on sex and on self-cognitive, self-reflective, and self-adjusting ability based on major. Furthermore, a notable correlation emerged between higher vocational college students' goal setting, self-management skills, and evaluating ability. This implies that improved goal setting leads to enhanced self-management skills and evaluating ability. Based on these findings, a student development plan was proposed to aid teachers in bolstering goal setting, self-management skills, and evaluating ability among Chinese higher vocational college students.

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Visual Analysis of the Time Management of Learning Multiple Courses in Online Learning Environment

Cited by 7 publications

References 17 publications

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

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

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

Goal Setting, self-management skills, and evaluating ability of Chinese higher vocational college students

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