scholarly journals Let’s Grow Together: Tutorials on Learning Analytics Methods

2016 ◽  
Vol 3 (3) ◽  
pp. 5-8 ◽  
Author(s):  
Dragan Gasevic ◽  
Mykola Pechenizkiy
Keyword(s):  
Network Analysis ◽  
Learning Analytics ◽  
Guest Editorial ◽  
Pattern Mining ◽  
Special Section ◽  
Visual Learning ◽  
Coherence Analysis ◽  
Text Coherence ◽  
Social Media Text ◽  
Visual Learning Analytics

This paper is a guest editorial into a special section that offers a collection of tutorials on methods that can be used in learning analytics. The special section is prepared as a response to the growing need of learning analytics practitioners and researchers to learn and use novel methods. In spite of this need, papers that systematically introduce some of the methods have been underrepresented in the literature. Specifically, the special section features papers that introduce epistemic network analysis, automated content and network analysis of social media, text coherence analysis with Coh-Metrix, microgenetic analysis with sequence pattern mining, and design of visual learning analytics guided by educational theory informed goals.

scholarly journals Improving Experienced Mathematics Teachers’ Classroom Talk: A Visual Learning Analytics Approach to Professional Development

2021 ◽  
Vol 13 (15) ◽  
pp. 8610
Author(s):  
Chung Kwan Lo ◽  
Gaowei Chen
Keyword(s):  
Professional Development ◽  
Activity Theory ◽  
Mathematics Teachers ◽  
Classroom Discourse ◽  
Learning Analytics ◽  
Visual Learning ◽  
Development Program ◽  
Experienced Teachers ◽  
Classroom Talk ◽  
Visual Learning Analytics

The professional development of experienced teachers has received considerably less attention than that of novice teachers. This study focuses on four experienced secondary mathematics teachers in Shanghai, China, with two participating in a year-long professional development program (treatment teachers) and the other two received conventional knowledge-based professional development (comparison teachers). The program introduced productive classroom talk skills which can facilitate teachers’ formative assessment of student learning during class. To encourage teachers to reflect on their classroom discourse when reviewing recordings of their teaching, we used visual learning analytics with the treatment teachers and theorized the use of this technology with activity theory. After completing the program, the treatment teachers were better able to use productive talk moves to elicit student responses and to provide timely formative feedback accordingly. Specifically, the percentage of word contributions in lessons from students and the length of their responses increased noticeably. Qualitative findings suggest that the use of visual learning analytics mediated the treatment teachers and improved classroom discourse. Based on these findings and activity theory, we provide recommendations for future use of visual learning analytics to improve teachers’ classroom talk and designing professional development activities for experienced teachers.

Guest Editorial: Special Section on Learning Analytics

2017 ◽  
Vol 10 (1) ◽  
pp. 3-5 ◽  
Author(s):  
Dragan Gasevic ◽  
George Siemens ◽  
Carolyn Penstein Rose
Keyword(s):  
Learning Analytics ◽  
Guest Editorial ◽  
Special Section

scholarly journals Exploring Software Engineering Subjects by Using Visual Learning Analytics Techniques

2015 ◽  
Vol 10 (4) ◽  
pp. 242-252 ◽  
Author(s):  
Miguel A. Conde ◽  
Francisco J. Garcia-Penalvo ◽  
Diego-Alonso Gomez-Aguilar ◽  
Roberto Theron
Keyword(s):  
Software Engineering ◽  
Learning Analytics ◽  
Visual Learning ◽  
Visual Learning Analytics

scholarly journals Shape of Educational Data: Interdisciplinary Perspectives

2017 ◽  
Vol 4 (2) ◽  
Author(s):  
Colleen M. Ganley ◽  
Sara A. Hart
Keyword(s):  
Student Learning ◽  
Educational Psychology ◽  
Learning Analytics ◽  
Guest Editorial ◽  
Online Courses ◽  
Special Section ◽  
Online Systems ◽  
The Third ◽  
Online Platforms ◽  
Insight Into

This paper is a guest editorial for a special section that forms the proceedings of the Shape of Educational Data meeting. This special section features papers that apply methods from multiple fields — including mathematics, computer science, educational psychology, and learning analytics — to describe and predict student learning in online platforms. The special section is organized such that the first set of articles discusses different online learning systems (WEPS, WeBWorK, and inVideo) and data that can be analyzed from these systems. The second set of articles involves descriptions of topological data analyses that can be helpful to researchers in learning analytics and educational psychology to better model student learning in online courses. The third set of articles uses data obtained from online systems to study factors related to student learning. Due to these multiple approaches, we can gain insight into the types of data available, the ways in which we can measure particular constructs related to learning using these data, and the ways we can analyze these data, including statistical approaches and visualizations.

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