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.

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 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.

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.

Considering Dimensions of the Digital Divide

2011 ◽  
pp. 869-883
Author(s):  
Thomas A. Lucey ◽  
Michael Grant
Keyword(s):  
Digital Divide ◽  
Computer Use ◽  
Public Information ◽  
Social Dimension ◽  
Social Challenges ◽  
The Social ◽  
Cultural Implications ◽  
And Gender ◽  
Ethnicity And Gender ◽  
School Computer

The Digital Divide refers to the social challenges of inequitable technology distribution and access. Educators must recognize that the digital divide extends beyond the ideas of physical allocation of computers and networks. The purpose of this chapter is to examine more closely the social, cultural and educational dimensions of the digital divide. The social dimension of the divide is considered with regard to societal access of public information and the Internet. An examination of cultural implications follows, highlighting ethnicity and gender issues. Educational dimensions examine literacies and online content, home computer use, school computer use and teacher readiness before presentation of recommendations for addressing these dimensions.

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