Applying learning analytics for improving students engagement and learning outcomes in an MOOCs enabled collaborative programming course

2018 ◽  
pp. 78-92
Keyword(s):  
Learning Outcomes ◽  
Learning Analytics ◽  
Collaborative Programming

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.

scholarly journals Leipzig’s Social Hypertext Reader SHRIMP and the “Introduction to American Studies”

2020 ◽  
Author(s):  
Sebastian M. Herrmann
Keyword(s):  
Learning Outcomes ◽  
Learning Analytics ◽  
American Studies ◽  
Learning Experience ◽  
Medium Of Instruction ◽  
Age Changes ◽  
Learning Platform ◽  
Bachelor Of Arts ◽  
Main Medium ◽  
E Learning

This article describes the ideas behind and the experiences with the experimental e-learning platform SHRIMP. Developed and deployed at American Studies Leipzig, the platform is used for the introductory Literature and Culture I seminar in the American Studies Bachelor of Arts program, and it serves as the main medium of instruction for around 80 students per year. It breaks up the linear form of the original seminar reader and instead offers students a hypertext of interconnected, short segments, enriched with social media and gamification elements, as well as a learning analytics component that invites students to take control of their own study and learning experience. It is driven by a dual assumption about digitization: that the digital age changes how students interact with text, and that digital textuality offers rich affordances beyond linear reading. Both can be harnessed to improve learning outcomes.

Connecting Teacher-Made Assessments to Course Learning Outcomes Through Learning Analytics

2019 ◽  
pp. 101-125
Author(s):  
Julia Chen ◽  
Dennis Foung
Keyword(s):  
Learning Outcomes ◽  
Learning Analytics ◽  
Structural Equation ◽  
Data Driven ◽  
Equation Modeling ◽  
Curriculum Alignment ◽  
Outcome Based Education ◽  
Data Driven Approach ◽  
The Subject

This chapter explores the possibility of adopting a data-driven approach to connecting teacher-made assessments with course learning outcomes. The authors begin by describing several key concepts, such as outcome-based education, curriculum alignment, and teacher-made assessments. Then, the context of the research site and the subject in question are described and the use of structural equation modeling (SEM) in this curriculum alignment study is explained. After that, the results of these SEM analyses are presented, and the various models derived from the analyses are discussed. In particular, the authors highlight how a data-driven curriculum model can benefit from input by curriculum leaders and how SEM provides insights into course development and enhancement. The chapter concludes with recommendations for curriculum leaders and front-line teachers to improve the quality of teacher-made assessments.

scholarly journals The evidence of impact and ethical considerations of Multimodal Learning Analytics: A Systematic Literature Review

2021 ◽  
Author(s):  
Haifa Alwahaby ◽  
Mutlu Cukurova ◽  
Zacharoula Papamitsiou ◽  
Michail Giannakos
Keyword(s):  
Literature Review ◽  
Learning Outcomes ◽  
Systematic Literature Review ◽  
Real World ◽  
Learning Analytics ◽  
Multimodal Learning ◽  
Ethical Considerations ◽  
Multimodal Data ◽  
Multimodal Learning Analytics ◽  
Evidence Of Impact

There is a growing interest in the research and use of multimodal data in learning analytics. This paper presents a systematic literature review of multimodal learning analytics (MMLA) research to assess i) the available evidence of impact on learning outcomes in real-world contexts and ii) explore the extent to which ethical considerations are addressed. A few recent literature reviews argue for the promising value of multimodal data in learning analytics research. However, our understanding of the challenges associated with MMLA research from real-world teaching and learning environments is limited. To address this gap, this paper provides an overview of the evidence of impact and ethical considerations stemming from an analysis of the relevant MMLA research published in the last decade. The search of the literature resulted in 663 papers, of which 100 were included in the final synthesis. The results show that the evidence of real-world impact on learning outcomes is weak, and ethical aspects of MMLA work are rarely addressed. We discuss our results through the lenses of two theoretical frameworks for evidence of impact types and ethical dimensions of MMLA. We conclude that for MMLA to stay relevant and become part of mainstream education, future research should directly address the gaps identified in this review.

scholarly journals Impact of GUI personalization of a word processor on a learning activity course

2020 ◽  
Author(s):  
Arnaud Zeller ◽  
Pascal Marquet
Keyword(s):  
User Interface ◽  
Learning Outcomes ◽  
Graphical User Interface ◽  
Learning Analytics ◽  
Word Processor ◽  
Learning Activity ◽  
Several Variables ◽  
Interaction Traces ◽  
The Impact ◽  
Writing Activity

This research investigates the impact of an activity of personalization of a graphical user interface by the learners, on their behavior of using the ILE. The analysis conducted is based on an exploitation of the interaction traces between the learner and the interface of a word processor software with advanced personalization and auto-writing features including training of spelling and a learning analytics management module. The results show that, several variables related to the facilitation conditions recognized by the ILE partly explain the writing activity. Navigation variable can be correlated with the knowledge of customization possibilities. If the automatic sentence generator has no significant effect on the number of misspellings found in the documents submitted, the intention to personalize the interface seems to have a greater effect than the act of personalization itself. But the impact of the personalization process on learning outcomes is still to be established. 

Digital learning approaches in an intermediate-level computer science course

2019 ◽  
Vol 36 (5) ◽  
pp. 467-484 ◽  
Author(s):  
Mikko Apiola ◽  
Erno Lokkila ◽  
Mikko-Jussi Laakso
Keyword(s):  
Computer Science ◽  
Learning Outcomes ◽  
Learning Analytics ◽  
Learning Systems ◽  
Intermediate Level ◽  
Future Research ◽  
Digital Learning ◽  
Learning Behaviour ◽  
Content Type ◽  
Practical Implications

Purpose Digital learning has become a global trend. Partly or fully automatic learning systems are integrated into education in schools and universities on a previously unseen scale. Learning systems have a lot of potential for re-education, life-long learning and for increasing access to educational resources. Learning systems create massive amounts of data about learning behaviour. Analysing that data for educational decision making has become an important track of research. The purpose of this paper is to analyse data from an intermediate-level computer science course, which was taught to 141 students in spring 2018 at University of Turku, Department of Future Technologies, Finland. Design/methodology/approach The available variables included number of submissions, submission times, variables of groupwork and final grades. Associations between these variables were looked at to reveal patterns in students’ learning behaviour. Findings It was found that time usage differs per different grades so that students with grade 4 out of 5 used most time. Also, it was found that studying at night is connected to weaker learning outcomes than studying during daytime. Several issues in relation to groupwork were revealed. For example, associations were found between prior skills, preference for individual vs groupwork, and course learning outcomes. Research limitations/implications The research was limited by the domain of available variables, which is a common limitation in learning analytics research. Practical implications The practical implications include important ideas for future research and interventions in digital learning. Social implications The importance of research on soft skills, social skills and collaboration is highlighted. Originality/value The paper points a number of important ideas for future research. One important observation is that some research questions in learning analytics need qualitative approaches, which need to be added to the toolbox of learning analytics research.

scholarly journals Discourse Centric Learning Analytics: Mapping the Terrain

2015 ◽  
Vol 2 (1) ◽  
Author(s):  
Simon Knight ◽  
Karen Littleton
Keyword(s):  
Learning Outcomes ◽  
Learning Analytics ◽  
Language Use ◽  
High Quality ◽  
Quality Learning ◽  
Large Groups ◽  
Core Issues

There is an increasing interest in developing learning analytic techniques for the analysis, and support of, high-quality learning discourse. This paper maps the terrain of discourse-centric learning analytics (DCLA), outlining the distinctive contribution of DCLA and outlining a definition for the field moving forwards. It is our claim that DCLA provides the opportunity to explore the ways in which discourse of various forms both resources and evidences learning; the ways in which small and large groups, and individuals, make and share meaning together through their language use; and the particular types of language — from discipline specific, to argumentative and socio-emotional — associated with positive learning outcomes. DCLA is thus not merely a computational aid to help detect or evidence “good” and “bad” performance (the focus of many kinds of analytics), but a tool to help investigate questions of interest to researchers, practitioners, and ultimately learners. The paper ends with three core issues for DCLA researchers — the challenge of context in relation to DCLA; the various systems required for DCLA to be effective; and the means through which DCLA might be delivered for maximum impact at the micro (e.g., learner), meso (e.g., school), and macro (e.g., government) levels.

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