Data-driven problem based learning: enhancing problem based learning with learning analytics

2020 ◽  
Vol 68 (6) ◽  
pp. 3393-3424
Author(s):  
Maria Zotou ◽  
Efthimios Tambouris ◽  
Konstantinos Tarabanis
Keyword(s):  
Learning Analytics ◽  
Problem Based Learning ◽  
Data Driven

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 Bridging the Gap between Theory and Empirical Research in Evaluative Judgment

2021 ◽  
pp. 1-16
Author(s):  
Hassan Khosravi ◽  
George Gyamfi ◽  
Barbara E. Hanna ◽  
Jason Lodge ◽  
Solmaz Abdi
Keyword(s):  
Higher Education ◽  
Empirical Research ◽  
Learning Analytics ◽  
Learning Activities ◽  
Data Driven ◽  
Educational Tool ◽  
Evaluative Judgment ◽  
Educational Tools ◽  
Education Literature

The value of students developing the capacity to accurately judge the quality of their work and that of others has been widely studied and recognized in higher education literature. To date, much of the research and commentary on evaluative judgment has been theoretical and speculative in nature, focusing on perceived benefits and proposing strategies seen to hold the potential to foster evaluative judgment. The efficacy of the strategies remains largely untested. The rise of educational tools and technologies that generate data on learning activities at an unprecedented scale, alongside insights from the learning sciences and learning analytics communities, provides new opportunities for fostering and supporting empirical research on evaluative judgment. Accordingly, this paper offers a conceptual framework and an instantiation of that framework in the form of an educational tool called RiPPLE for data-driven approaches to investigating the enhancement of evaluative judgment. Two case studies, demonstrating how RiPPLE can foster and support empirical research on evaluative judgment, are presented.

scholarly journals Curricular Design Analysis: A Data-Driven Perspective

2014 ◽  
Vol 1 (3) ◽  
pp. 84-119 ◽  
Author(s):  
Gonzalo Mendez ◽  
Xavier Ochoa ◽  
Katherine Chiluiza ◽  
Bram De Wever
Keyword(s):  
Academic Performance ◽  
Computer Science ◽  
Learning Process ◽  
Learning Analytics ◽  
Data Driven ◽  
Educational Institutions ◽  
Science Program ◽  
Curricular Design ◽  
Micro Level ◽  
Course Difficulty

Learning analytics has been as used a tool to improve the learning process mainly at the micro-level (courses and activities).  However, another of the key promises of Learning Analytics research is to create tools that could help educational institutions at the meso- and macro-level to gain a better insight of the inner workings of their programs, in order to tune or correct them. This work presents a set of simple techniques that applied to readily available historical academic data could provide such insights. The techniques described are real course difficulty estimation, course impact on the overall academic performance of students, curriculum coherence, dropout paths and load/performance graph. The usefulness of these techniques is validated through their application to real academic data from a Computer Science program. The results of the analysis are used to obtain recommendations for curriculum re-design.

scholarly journals Demystification of Artificial Intelligence in Education – How much AI is really in the Educational Technology?

2020 ◽  
Vol 2 (1) ◽  
pp. 14 ◽  
Author(s):  
André Renz ◽  
Swathi Krishnaraja ◽  
Elisa Gronau
Keyword(s):  
Artificial Intelligence ◽  
Educational Technology ◽  
Learning Analytics ◽  
Public Discourse ◽  
Business Models ◽  
Data Driven ◽  
Current Status ◽  
Causal Connection ◽  
Artificial Intelligence In Education ◽  
The Relationship

<span lang="EN-US">The data-driven development of education through Learning Analytics in combination with Artificial Intelligence is an emerging field in the education sector. In the field of Artificial Intelligence in Education, numerous studies and research have been carried out over the past 60 years, and since then drastic changes have taken place. In the first part of this paper we present a brief overview of the current status of Learning Analytics and Artificial Intelligence in education. In order to develop a better understanding of the relationship between Learning Analytics and Artificial Intelligence in education, we outline the relationship between the two phenomena. The results show that the previous studies only vaguely distinguish between them: the terms are often used synonymously. In the second part of the paper we focus on the question why the European market currently has hardly any real applications for Artificial Intelligence in education. The research is based on a meta-investigation of data-driven business models, in particular the so-called Educational Technology providers. The core of the analysis is the question of how data-driven these companies really are, how much Learning Analytics and Artificial Intelligence is applied and whether there is a causal connection between the growth of the Educational Technology market and the application relevance of Artificial Intelligence in Education. In the scientific and public discourse, we can observe a distortion between the theoretical-conjunctive understanding of the application of Artificial Intelligence in Education and the current practical relevance.</span>

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