The teaching and learning analytics for Using Mutimedia-Assisted

2017 ◽  
Vol 67 ◽  
pp. 213-235
Author(s):  
Kil-Yong Oh
Keyword(s):  
Learning Analytics ◽  
Teaching And Learning

scholarly journals Learning Analytics in a Virtual Learning Environment: the challenge of mapping socio-affective scenarios

2020 ◽  
Vol 8 (6) ◽  
pp. 190-204
Author(s):  
Jacqueline Mayumi Akazaki ◽  
Leticia Rocha Machado ◽  
Ketia Kellen Araújo da Silva ◽  
Patricia Alejandra Behar
Keyword(s):  
Social Interactions ◽  
Undergraduate Students ◽  
Social Relations ◽  
Learning Analytics ◽  
Teaching And Learning ◽  
Virtual Learning ◽  
Virtual Learning Environment ◽  
Pedagogical Strategies ◽  
The Social ◽  
Visualization Of Data

Virtual courses are increasingly being offered in Brazil, making it imperative to develop technological resources and research to help in the teaching and learning processes in this modality. One approach is to analyze student's socio-affective profile in Virtual Learning Environments (VLE). The co-operative learning network (ROODA) VLE has two features called the Social Map (SM) and Affective Map (AM), which can both contribute to the visualization of data regarding social interaction indicators and students' moods in the environment. The SM presents the social relations formed through indicators, which are the absence; collaboration; the distance from the class; evasion; informal groups and popularity, enabling the identification of the participating subjects in the form of sociograms. The AM identifies students' moods graphically through indicators, which are excitement, discouragement, satisfaction, and dissatisfaction. Thus, this article aims to map the possible recurrent socio-affective scenarios in a VLE using Learning Analytics (LA). LA is defined as measurement, collection, analysis, and reporting of data about students and their contexts to understand as well as optimize learning and the environments in which it occurs. It can also contribute to the understanding of student's learning profile, based on social and affective aspects, thus allowing the teacher to develop pedagogical strategies consistent with the needs of each subject. The importance of integrating the possible social and affective scenarios was verified using LA, making it possible to deepen the comprehension of the subjective and qualitative questions regarding the students' interactions in the VLE. In this study, the scenarios are understood as the intersection between the Affective Map and Social Map indicators identified in a VLE. It has both a qualitative and quantitative approach. The choice is qualitatively justified because the research object involves social and affective phenomena that were subjectively expressed in texts and social interactions manifested in the ROODA VLE. It is quantitatively justified by the need to measure the mapping of socio-affective indicators through social parameters and moods applying LA. The subjects were undergraduate students who participated in distance learning courses at a Brazilian public university that used the ROODA VLE in the second semester of 2019. Data were collected from social and affective maps to identify if there was a relationship between them. As a result, based on the existing indicators of social interactions and moods, the socio-affective indicators were created using LA in order to analyze the students’ behavior in relation to the forms of interaction and communication that occur in the ROODA VLE.

scholarly journals OnTask

2020 ◽  
Vol 2 (1) ◽  
pp. 42
Author(s):  
Steve Leichtweis
Keyword(s):  
Learning Analytics ◽  
Teaching And Learning ◽  
Teaching Practice ◽  
Course Design ◽  
Learning Experience ◽  
Technology Enhanced Learning ◽  
Learning Approaches ◽  
Laptop Computer ◽  
Data Set ◽  
Hands On

Universities are increasingly being expected to ensure student success while at the same time delivering larger courses.  Within this environment, the provision of effective and timely feedback to students and creating opportunities for genuine engagement between teachers and students is increasingly difficult if not impossible for many instructors, despite the known value and importance of feedback (Timperley & Hattie, 2007) and instructor presence (Garrison, Anderson & Archer, 2010).  Similar to other tertiary institutions, the University of Auckland has adopted various technology-enhanced learning approaches and technologies, including learning analytics in an attempt to support teaching and learning at scale.  The increased use of educational technology to support learning provides a variety of data sources for teachers to provide personalised feedback and improve the overall learning experience for students.  This workshop is targeted to teachers interested in the use of learning data to provide personalized support to learners.  Participants will have a hands-on opportunity to use the open-source tool OnTask (Pardo, et al. 2018) within some common teaching scenarios with a synthetically generated data set.  The facilitators will also share and discuss how OnTask is currently being used in universities to support student experience, teaching practice and course design.  As this is a hands-on workshop, participants must bring a laptop computer to work with the online tool and the prepared scenarios.  References   Garrison, D. R., Anderson, T., & Archer, W. (2010). The first decade of the community of inquiry framework: A retrospective. The internet and higher education, 13(1-2), 5-9. Hattie, J., & Timperley, H. (2007). The power of feedback. Review of educational research, 77(1), 81-112. Pardo, A., Bartimote-Aufflick, K., Shum, S. B., Dawson, S., Gao, J., Gaševic, D., Leichtweis, S., Liu, D., Martínez-Maldonado, R., Mirriahi, N. and Moskal, A. C. M. (2018). OnTask: Delivering Data-Informed, Personalized Learning Support Actions. Journal of Learning Analytics, 5(3), 235-249.

scholarly journals An Instructor Learning Analytics Implementation Model

2017 ◽  
Vol 21 (3) ◽  
Author(s):  
Holly McKee
Keyword(s):  
Learning Outcomes ◽  
Learning Analytics ◽  
Teaching And Learning ◽  
Research Problem ◽  
Self Reflection ◽  
Implementation Model ◽  
Design And Development Research ◽  
Pedagogical Intent ◽  
And Performance ◽  
Support Students

With the widespread use of learning analytics tools, there is a need to explore how these technologies can be used to enhance teaching and learning. Little research has been conducted on what human processes are necessary to facilitate meaningful adoption of learning analytics. The research problem is that there is a lack of evidence-based guidance on how instructors can effectively implement learning analytics to support students with the purpose of improving learning outcomes. The goal was to develop and validate a model to guide instructors in the implementation of learning analytics tools. Using design and development research methods, an implementation model was constructed and validated internally. Themes emerged falling into the categories of adoption and caution with six themes falling under adoption including: LA as evidence, reaching out, frequency, early identification/intervention, self-reflection, and align LA with pedagogical intent and three themes falling under the category of caution including: skepticism, fear of overdependence, and question of usefulness.  The model should enhance instructors’ use of learning analytics by enabling them to better take advantage of available technologies to support teaching and learning in online and blended learning environments. Researchers can further validate the model by studying its usability (i.e., usefulness, effectiveness, efficiency, and learnability), as well as, how instructors’ use of this model to implement learning analytics in their courses affects retention, persistence, and performance.

Integrating Assessment, Feedback, and Learning Analytics in Educational Games

2019 ◽  
pp. 127-169
Author(s):  
Yaëlle Chaudy ◽  
Thomas M. Connolly
Keyword(s):  
Learning Process ◽  
Learning Analytics ◽  
Teaching And Learning ◽  
Assessment Tool ◽  
Educational Games ◽  
Assessment Process ◽  
Crucial Aspect ◽  
Assessment Feedback ◽  
Black Boxes ◽  
Literature Reviews

Assessment is a crucial aspect of any teaching and learning process. New tools such as educational games offer promising advantages: they can personalize feedback to students and save educators time by automating the assessment process. However, while many teachers agree that educational games increase motivation, learning, and retention, few are ready to fully trust them as an assessment tool. A likely reason behind this lack of trust is that educational games are distributed as black boxes, unmodifiable by educators and not providing enough insight about the gameplay. This chapter presents three systematic literature reviews looking into the integration of assessment, feedback, and learning analytics in educational games. It then proposes a framework and present a fully developed engine. The engine is used by both developers and educators. Designed to separate game and assessment, it allows teachers to modify the assessment after distribution and visualize gameplay data via a learning analytics dashboard.

Educational Data Mining Techniques and Applications

2021 ◽  
pp. 234-251
Author(s):  
M. Govindarajan
Keyword(s):  
Data Mining ◽  
Learning Analytics ◽  
Teaching And Learning ◽  
At Risk Students ◽  
Educational Data Mining ◽  
Learning Needs ◽  
Institutional Performance ◽  
Clear Cut ◽  
Curriculum Renewal ◽  
Improving Teaching And Learning

Educational data mining (EDM) creates high impact in the field of academic domain. EDM is concerned with developing new methods to discover knowledge from educational and academic database and can be used for decision making in educational and academic systems. EDM is useful in many different areas including identifying at risk students, identifying priority learning needs for different groups of students, increasing graduation rates, effectively assessing institutional performance, maximizing campus resources, and optimizing subject curriculum renewal. This chapter discusses educational data mining, its applications, and techniques that have to be adopted in order to successfully employ educational data mining and learning analytics for improving teaching and learning. The techniques and applications discussed in this chapter will provide a clear-cut idea to the educational data mining researchers to carry out their work in this field.

Integrating Assessment, Feedback, and Learning Analytics in Educational Games

2022 ◽  
pp. 1803-1846
Author(s):  
Yaëlle Chaudy ◽  
Thomas M. Connolly
Keyword(s):  
Learning Process ◽  
Learning Analytics ◽  
Teaching And Learning ◽  
Assessment Tool ◽  
Educational Games ◽  
Assessment Process ◽  
Crucial Aspect ◽  
Assessment Feedback ◽  
Black Boxes ◽  
Literature Reviews

Assessment is a crucial aspect of any teaching and learning process. New tools such as educational games offer promising advantages: they can personalize feedback to students and save educators time by automating the assessment process. However, while many teachers agree that educational games increase motivation, learning, and retention, few are ready to fully trust them as an assessment tool. A likely reason behind this lack of trust is that educational games are distributed as black boxes, unmodifiable by educators and not providing enough insight about the gameplay. This chapter presents three systematic literature reviews looking into the integration of assessment, feedback, and learning analytics in educational games. It then proposes a framework and present a fully developed engine. The engine is used by both developers and educators. Designed to separate game and assessment, it allows teachers to modify the assessment after distribution and visualize gameplay data via a learning analytics dashboard.

Learning Analytics as a Tool for Planning, Evaluating, and Improving a SMOOC for Teacher Professional Development

2021 ◽  
pp. 170-189
Author(s):  
Cristine Martins Gomes de Gusmão ◽  
Josiane Lemos Machiavelli ◽  
Patricia Smith Cavalcante
Keyword(s):  
Teacher Professional Development ◽  
Learning Analytics ◽  
Online Teaching ◽  
Teaching And Learning ◽  
Pedagogical Issues ◽  
Educational Culture ◽  
Learning Contexts ◽  
Teacher Professional ◽  
Teaching Learning ◽  
The University

This chapter describes how a public university has met the challenge of changing inside the educational culture and preparing its teachers to manage online teaching-learning processes using learning analytics to contribute to the design, evaluation, and improvement of SMOOC. From the results obtained with a survey answered by the teachers, a twenty-hour class SMOOC was developed that focuses on instrumental aspects of digital technological resources in the teaching and learning contexts, as well as in the pedagogical issues, which concern the appropriate use of digital technologies. The findings of this research demonstrate that the SMOOC has been able to meet the teacher training needs, which are changing the way they work since remote education has been the viable alternative to guarantee the functioning of the university in the coronavirus pandemic period. However, it is believed that the knowledge that teachers are acquiring will contribute to changes in professional practice even post-pandemic.

scholarly journals Critical Data Studies, Abstraction & Learning Analytics

2019 ◽  
Vol 6 (3) ◽  
Author(s):  
Simon Buckingham Shum
Keyword(s):  
Social Science ◽  
Learning Analytics ◽  
Teaching And Learning ◽  
Special Section ◽  
Keynote Address ◽  
Critical Data

This editorial introduces a special section of the Journal of Learning Analytics, for which Neil Selwyn’s keynote address to LAK ’18 has been written up as an article, “What’s the problem with learning analytics?” His claims and arguments are engaged in commentaries from Alfred Essa, Rebecca Ferguson, Paul Prinsloo, and Carolyn Rosé, who provide diverse perspectives on Selwyn’s proposals and arguments, from applause to refutation. Reflecting on the debate, I note some of the tensions to be resolved for learning analytics and social science critiques to engage productively, observing that central to the debate is how we understand the role of abstraction in the analysis of data about teaching and learning, and hence the opportunities and risks this entails.  

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