Learning Analytics of the Relationships among Learning Behaviors, Learning Performance, and Motivation

2020 ◽  
Author(s):  
Xuewang Geng ◽  
Yufan Xu ◽  
Li Chen ◽  
Hiroaki Ogata ◽  
Atsushi Shimada ◽  
...  
Keyword(s):  
Learning Analytics ◽  
Learning Performance ◽  
Learning Behaviors

scholarly journals Direction of collaborative problem solving-based STEM learning by learning analytics approach

2019 ◽  
Vol 14 (1) ◽  
Author(s):  
Li Chen ◽  
Nobuyuki Yoshimatsu ◽  
Yoshiko Goda ◽  
Fumiya Okubo ◽  
Yuta Taniguchi ◽  
...  
Keyword(s):  
Problem Solving ◽  
Learning Strategies ◽  
Stem Education ◽  
Learning Analytics ◽  
Learning Strategy ◽  
Collaborative Problem Solving ◽  
Learning Performance ◽  
Stem Learning ◽  
Learning Behaviors ◽  
Collaborative Problem

AbstractThe purpose of this study was to explore the factors that might affect learning performance and collaborative problem solving (CPS) awareness in science, technology, engineering, and mathematics (STEM) education. We collected and analyzed data on important factors in STEM education, including learning strategy and learning behaviors, and examined their interrelationships with learning performance and CPS awareness, respectively. Multiple data sources, including learning tests, questionnaire feedback, and learning logs, were collected and examined following a learning analytics approach. Significant positive correlations were found for the learning behavior of using markers with learning performance and CPS awareness in group discussion, while significant negative correlations were found for some factors of STEM learning strategy and learning behaviors in pre-learning with some factors of CPS awareness. The results imply the importance of an efficient approach to using learning strategies and functional tools in STEM education.

Missing Piece in Understanding Student Learning: Out-of-School Computer Use

2018 ◽  
Vol 57 (2) ◽  
pp. 320-342 ◽  
Author(s):  
Xiaoqing Gu ◽  
Hongjin Xu
Keyword(s):  
Student Learning ◽  
Computer Use ◽  
Learning Analytics ◽  
Learning Performance ◽  
Learning Behaviors ◽  
Multiple Data ◽  
School Learning ◽  
And Gender ◽  
School Computer ◽  
Out Of School

Advancements in learning analytics allow teachers to track student learning progress and promote learning by providing necessary intervention and support. Multiple data sources are involved in learning analytics, and the major ones are systems that students use in school. To fully comprehend the progress of student learning, out-of-school learning behaviors should be considered an important part of the academic lives of students. In this study, out-of-school learning behaviors of students, particularly home computer use, were measured using four online behavior indicators of students, which were tracked and collected. The learning performance data of the students were analyzed. Results suggested that the out-of-school computer use behaviors of students, such as mutual follow-up and the sharing of learning experiences, were positively related to their academic performance level, regardless of the age and gender of the students. This study provides insight into what may be the missing piece in understanding student learning, that is, out-of-school computer use. With such insights, learning analytics may be enhanced to improve the understanding of learning without being restricted to schools.

scholarly journals Learning Analytics to Inform the Learning Design: Supporting Instructor’s Inquiry into Student Learning in Unsupervised Technology-Enhanced Platforms

2019 ◽  
Vol 23 (3) ◽  
Author(s):  
Priya Harindranathan ◽  
James Folkestad
Keyword(s):  
Student Learning ◽  
Learning Strategies ◽  
Learning Analytics ◽  
Formative Assessments ◽  
Technology Enhanced Learning ◽  
Learning Tools ◽  
Learning Behaviors ◽  
Pedagogical Design ◽  
Actual Use ◽  
Learning Platforms

Instructors may design and implement formative assessments on technology-enhanced platforms (e.g., online quizzes) with the intention of encouraging the use of effective learning strategies like active retrieval of information and spaced practice among their students. However, when students interact with unsupervised technology-enhanced learning platforms, instructors are often unaware of students’ actual use of the learning tools with respect to the pedagogical design. In this study, we designed and extracted five variables from the Canvas quiz-log data, which can provide insights into students’ learning behaviors. Anchoring our conceptual basis on the ‘influential conversational framework’, we find that learning analytics (LA) can provide instructors with critical information related to students’ learning behaviors, thereby supporting instructors’ inquiry into student learning in unsupervised technology-enhanced platforms. Our findings suggest that the information that LA provides may enable instructors to provide meaningful feedback to learners and improve the existing learning designs.

Regulatory focus and learning

2020 ◽  
Vol 44 (4/5) ◽  
pp. 425-447 ◽  
Author(s):  
Jessica E. Federman
Keyword(s):  
Informal Learning ◽  
Regulatory Focus ◽  
Structured Interview ◽  
Learning Performance ◽  
Prevention Focus ◽  
Promotion Focus ◽  
Learning Behaviors ◽  
Content Type ◽  
Differential Effects ◽  
Performance Success

Purpose The purpose of this study is to understand how regulatory focus influences informal learning behaviors. A growing body of research indicates that regulatory focus has significant consequences for goal pursuit in the workplace, yet it has not been readily studied or applied to the field of human resource management (Johnson et al., 2015). This is one of the few studies to examine the relationship between informal learning and regulatory focus theory that can be applied to the training and development field. Design/methodology/approach Using a qualitative research design, a semi-structured interview was used to increase the comparability of participant responses. Questions were asked in an open-ended manner, allowing for a structured approach for collecting information yet providing flexibility for the sake of gaining more in-depth responses. An interview guideline was used to standardize the questions and ensure similar kinds of information were obtained across participants. A typological analytic approach (Lincoln and Guba, 1985) was used to analyze the data. Findings In a sample of 16 working adults, (44% female and 56% male), participants who were identified as having either a promotion- or prevention-focus orientation were interviewed about types of informal learning strategies they used. The results revealed that performance success and failure have differential effects on learning behaviors for prevention and promotion-focus systems. Stress and errors motivate informal learning for the prevention-focus system, whereas positive affect motivates informal learning for the promotion-focus system. Prevention-focus participants articulated greater use of vicarious learning, reflective thinking and feedback-seeking as methods of informal learning. Promotion-focus participants articulated greater use of experimentation methods of informal learning. Originality/value This study provides an in-depth understanding of how regulatory focus influences informal learning. Few studies have considered how regulatory focus promotes distinct strategies and inclinations toward using informal learning. Performance success and failure have differential effects on informal learning behaviors for regulatory promotion and prevention systems. This has theoretical and practical implications in consideration of why employees engage in informal learning, and the tactics and strategies they use for learning.

scholarly journals Exploring jump back behavior patterns and reasons in e-book system

2022 ◽  
Vol 9 (1) ◽  
Author(s):  
Boxuan Ma ◽  
Min Lu ◽  
Yuta Taniguchi ◽  
Shin’ichi Konomi
Keyword(s):  
Student Learning ◽  
Activity Patterns ◽  
Learning Performance ◽  
Digital Learning ◽  
Stream Data ◽  
Learning Materials ◽  
Reading Behaviors ◽  
Log Data ◽  
Learning Behaviors ◽  
Event Stream

AbstractWith the increasing use of digital learning materials in higher education, the accumulated operational log data provide a unique opportunity to analyzing student learning behaviors and their effects on student learning performance to understand how students learn with e-books. Among the students’ reading behaviors interacting with e-book systems, we find that jump-back is a frequent and informative behavior type. In this paper, we aim to understand the student’s intention for a jump-back using user learning log data on the e-book materials of a course in our university. We at first formally define the “jump-back” behaviors that can be detected from the click event stream of slide reading and then systematically study the behaviors from different perspectives on the e-book event stream data. Finally, by sampling 22 learning materials, we identify six reading activity patterns that can explain jump backs. Our analysis provides an approach to enriching the understanding of e-book learning behaviors and informs design implications for e-book systems.

scholarly journals Analyzing Learning Patterns Based on Log Data from Digital Textbooks

2019 ◽  
Vol 17 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Kousuke Mouri ◽  
Zhuo Ren ◽  
Noriko Uosaki ◽  
Chengjiu Yin
Keyword(s):  
Learning Style ◽  
Cognitive Styles ◽  
Learning Performance ◽  
Apriori Algorithm ◽  
Analysis Method ◽  
Log Data ◽  
Learning Behaviors ◽  
Learning And Teaching ◽  
Learning Patterns ◽  
Digital Textbooks

The analysis of learning behaviors from the log data of digital textbooks is beneficial for improving education systems. The focus of discussion in any analysis of learning behaviors is often on discovering the relationships between learning behavior and learning performance. However, little attention has been paid to investigating and analyzing learning patterns or rules among learning style of index (LSI), cognitive style of index (CSI), and the logs of digital textbooks. In this study, the authors proposed a method to analyze learning patterns or rules of reading digital textbooks. The analysis method used association analysis with the Apriori algorithm. The analysis was conducted using logs of digital textbooks and questionnaires to investigate students' learning and cognitive styles. From the detected meaningful association rules, this study found three student types: poorly motivated, efficient, and diligent. The authors believe that consideration of these student types can contribute to the improvement of learning and teaching

Adopting Learning Analytics in a First-Year Veterinarian Professional Program: What We Could Know in Advance about Student Learning Progress

2021 ◽  
Vol 48 (6) ◽  
pp. 720-728
Author(s):  
Wenting Weng ◽  
Nicola L. Ritter ◽  
Karen Cornell ◽  
Molly Gonzales
Keyword(s):  
At Risk ◽  
Academic Success ◽  
Student Learning ◽  
Learning Analytics ◽  
Prediction Models ◽  
Early Stage ◽  
At Risk Students ◽  
Learning Performance ◽  
First Year ◽  
Actual Performance

Over the past decade, the field of education has seen stark changes in the way that data are collected and leveraged to support high-stakes decision-making. Utilizing big data as a meaningful lens to inform teaching and learning can increase academic success. Data-driven research has been conducted to understand student learning performance, such as predicting at-risk students at an early stage and recommending tailored interventions to support services. However, few studies in veterinary education have adopted Learning Analytics. This article examines the adoption of Learning Analytics by using the retrospective data from the first-year professional Doctor of Veterinary Medicine program. The article gives detailed examples of predicting six courses from week 0 (i.e., before the classes started) to week 14 in the semester of Spring 2018. The weekly models for each course showed the change of prediction results as well as the comparison between the prediction results and students’ actual performance. From the prediction models, at-risk students were successfully identified at the early stage, which would help inform instructors to pay more attention to them at this point.

scholarly journals Learning Analytics: Analyzing Various Aspects of Learners’ Performance in Blended Courses. The Case of Kabul Polytechnic University, Afghanistan

2020 ◽  
Vol 15 (12) ◽  
pp. 168
Author(s):  
Hamidullah Sokout ◽  
Tsuyoshi Usagawa ◽  
Sohail Mukhtar
Keyword(s):  
Learning Analytics ◽  
Teaching And Learning ◽  
Learning Performance ◽  
Discussion Forums ◽  
Educational Organizations ◽  
Blended Courses ◽  
University Instructors ◽  
Instructor Practices ◽  
Academic Lives

Learning performance is crucial in students’ academic lives because it opens opportunities for future professional development. However, conventional educational practices do not provide all the necessary skills for university instructors and students to succeed in today's educational context. In addition, due to poor information resources, ineffective ICT tool utilization and the teaching methodologies in developing countries, particularly Afghanistan, a large gap exists across curriculum plans and instructor practices. Learning analytics, as a new educational instrument, has made it possible for higher education actors to reshape the educational environment to be more effective and consistent. In this study, we analyzed multiple research approaches and the results of analytics of various learner aspects to address the aforementioned issues. The research methods were predominantly quantitative-cum-qualitative. Real (quantitative) data were collected based on learners’ explicit actions, such as completing assignments and taking exams, and implicit actions, such interacting and posting on discussion forums. Meanwhile, secondary (qualitative) data collection was conducted on-site at Kabul Polytechnic University (KPU); both blended and traditional class samples were included. The results of this study offer insight into various aspects of learners’ behaviors that lead to their success and indicate the best analytical model/s to provide the highest prediction accuracy. Furthermore, the results of this study could help educational organizations adopt learning analytics to conduct early assessments to evaluate the quality of teaching and learning and improve learners’ performance.

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