Studies have shown that there is a gap between theory and practice in the use of learning analytics in educational settings. Some researchers attribute this gap to not taking learning theories into consideration in the use of learning analytics in educational contexts. This study was conducted to address the role of learning theory in applying learning analytic in educational contexts.
This is a qualitative study and the study design is content analysis. Thematic analysis was used as the research method. Data for this study was collected through an interview with 14 experts in the fields of learning analytic and learning theory who were selected purposefully. The theoretical saturation method was used to determine the sample size. Content analysis techniques were used to analyze data and content validity index (CVI) and Cohen’s kappa coefficient were performed to measure the validity and reliability of the findings.
Data analysis was performed to identify three main roles for learning theory in learning analytic including underpinning role, guiding role, and sense-making role.
The results suggest that first, learning theory should underlie learning analytics (where to begin). Second, the application of learning analytics in educational settings should be guided by learning theory (what and how to do), and third, learning analytics’ reports should be interpreted based on the learning theory implications for education (answer to the question why).
Learning theory, Learning analytic, Thematic analysis
In the digital age, we come across a new generation that is called “digital natives” (1). Technology Enhanced Learning (TEL) is a key concept for the education of digital natives, and thanks to the technology, we are now able more than ever to track data about learners and teachers in learning contexts. Data plays a key role in today’s education and some declare a data-driven approach in education (2-4). Siemens and Long (5) point out that big data and analytics are two critical keywords of future education. In view of the ever-growing attention to data and analytics, the field of Learning Analytics (LA) was born in 2011 to measure, collect, analyze and report data about learners and their context for the purpose of understanding and optimizing learning and the environments in which it occurs (5).
Studies have demonstrated that learning analytics could offer noticeable advantages and useful insights to boost education. For example, learning analytics could inform teachers about the learning process, identify students at risk of failure, increase students’ retention rate, provide real-time feedback for teachers and learners, identify students’ learning habits, improve learning design, provide evidence-based decisions, enhance students’ engagement, provide insights about students’ interaction and social networks, and improve personalized learning (6-13). But, this promising and newborn field of study suffers from a gap between its theory and practice (14). Siemens (13) and Knight, Buckingham Shum, & Littleton (15) mention the threat of technological and mechanical determinism in the use of learning analytics tools in educational settings. Studies have shown that educational innovations would not be introduced successfully by simply providing access to new tools (16-18). New technologies in education would be used marginally if there is no plan to shift patterns of teaching and learning activities. Introducing new technologies in education is in need of being passed through the filter of educational foundations. Gašević et al (19) state that general learning analytics models have less reliable academic success predictions, while course-specific learning analytics models could offer more reliable results. It means one learning analytics model might not fit all learning contexts.
As a multidisciplinary field of study, learning analytics borrows ideas from different areas such as statistics, computer science, machine learning, pedagogy, business intelligence, and learning science (8). But, the joining point of all these areas in learning analytics is the concept of learning. Learning analytics is about learning (20) and the purpose of learning analytics is to optimize and improve learning (5). We need to understand what learning means in different contexts to appropriately use learning analytics (20). Also, learning theory plays an underlying role in the teaching and learning process, because different learning theories have their own specific educational implications (21). For example, the learning and teaching process based on the connectivism theory is different compared to cognitivist learning theory.
From a connectivity perspective, networks and nodes are the two key factors (22, 23) while in cognitivism, other factors such as memory (coding, encoding, and retrieval), previous knowledge, mental structure, and schema are playing important roles (24). Or behaviorists believe that external motivations like rewards and reinforcements are the factors that influence learning, and consider thoughts, perceptions, memory, or consciousness as a black box which cannot be measured or observed (25- 27) while constructivists consider learning as a personal construction of knowledge (28, 29) and mention engagement, collaboration, problem-solving, social and cultural settings as factors that influence learning (30, 31).
This is apparently the reason why learning theories are important in potentially filling the gap between theory and practice in the use of learning analytics in educational settings. A clear understanding of learning could help the users of learning analytics to know what exactly they should be looking to capture, analyze and report and how these findings should be interpreted to properly inform learning analytics stakeholders such as teachers and learners. This study takes learning theory in general into account and tries to bridge the gap between theory and practice in learning analytics. Based on the above-mentioned points, this study aims to answer the following question: What is the role of learning theory in applying learning analytics in educational contexts?
To conclude, it is said that this study might make contributions to understand the importance of the word “learning” and the “learning approach” in learning analytics as it tends to be the core focus of any learning analytics activities. This qualitative study was an initial attempt at outlining the role of learning theory in the use of learning analytics in educational contexts, and further theoretical and empirical studies are needed.
The Kavian Scientific Research Association (KSRA) is a non-profit research organization to provide research / educational services in December 2013. The members of the community had formed a virtual group on the Viber social network. The core of the Kavian Scientific Association was formed with these members as founders. These individuals, led by Professor Siavosh Kaviani, decided to launch a scientific / research association with an emphasis on education.
KSRA research association, as a non-profit research firm, is committed to providing research services in the field of knowledge. The main beneficiaries of this association are public or private knowledge-based companies, students, researchers, researchers, professors, universities, and industrial and semi-industrial centers around the world.
Our main services Based on Education for all Spectrum people in the world. We want to make an integration between researches and educations. We believe education is the main right of Human beings. So our services should be concentrated on inclusive education.
The KSRA team partners with local under-served communities around the world to improve the access to and quality of knowledge based on education, amplify and augment learning programs where they exist, and create new opportunities for e-learning where traditional education systems are lacking or non-existent.
FULL Paper PDF file:Investigation on the Role of Learning Theory in Learning Analytic
Investigation on the Role of Learning Theory in Learning Analytics
Interdisciplinary Journal of Virtual Learning in Medical Sciences
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Nasim Gazerani was born in 1983 in Arak. She holds a Master's degree in Software Engineering from UM University of Malaysia.
Professor Siavosh Kaviani was born in 1961 in Tehran. He had a professorship. He holds a Ph.D. in Software Engineering from the QL University of Software Development Methodology and an honorary Ph.D. from the University of Chelsea.
Somayeh Nosrati was born in 1982 in Tehran. She holds a Master's degree in artificial intelligence from Khatam University of Tehran.