Generating personalized learning pathways for e-learners is a critical issue in the field of e-learning as it plays a pivotal role in guiding learners towards the successful achievement of their learning objectives. The existing literature has proposed various methods from different perspectives to address this issue, including learner-based,…

Educators and researchers have analyzed various image data acquired from teaching and learning, such as images of learning materials, classroom dynamics, students' drawings, etc. However, this approach is labour-intensive and time-consuming, limiting its scalability and efficiency. The recent development in the Visual Question Answering (VQA)…

This paper deeply discusses the transformation potential of integrating Internet big data into the pre-school education model in colleges and universities. Through in-depth analysis, we studied the challenges and opportunities faced by preschool education in colleges and universities, and discussed the innovative influence of big data technology…

The need for data-driven decision-making primarily motivates interest in analysing Big Data in higher education. Although there has been considerable research on the value of Big Data in higher education, its application to address critical issues within the sector is still limited. This systematic review, conducted in December 2021 and…

Cloud computing, internet of things (IoT), artificial intelligence, and big data are four very different technologies that are already discussed separately. The use of the four technologies is required to be more and more necessary in the present day in order to make them important components in today's world technology. In this paper, the authors…

Amidst ongoing technological and social change, this article explores the implications for critical education that result from a data-driven model of digital governance. The article argues that traditional notions of critique which rely upon the deconstruction and analysis of texts are increasingly redundant in the age of datafication, where the…

Ten years after the announcement of the "rise of the super experiment" at Educational Data Mining 2012, challenges to implementing "internet scale" educational experiments often persist for educational technology providers, especially when they seek to test substantive instructional interventions. Studies that deploy and test…

This article describes experiences with teaching process data analytics and machine learning, including in: (1) a joint undergraduate/graduate course for students in chemical and mechanical engineering and engineering management; and (2) an undergraduate chemical engineering concentration in process data analytics. The article also describes…

The constantly growing offering of online learning materials to students is making it more difficult to locate specific information from data pools. Personalization systems attempt to reduce this complexity through adaptive e-learning and recommendation systems. The latter are, generally, based on machine learning techniques and algorithms and…

The proliferation of vast quantities of available datasets that are large and complex in nature has challenged universities to keep up with the demand for graduates trained in both the statistical and the computational set of skills required to effectively plan, acquire, manage, analyze, and communicate the findings of such data. To keep up with…

Increasingly, students training in the biological sciences depend on a proper grounding in biological statistics, data science and experimental design. As biological datasets increase in size and complexity, transparent data management and analytical methods are essential skills for undergraduate biologists. We propose that using the software R…

New technologies, including artificial intelligence (AI), enablers of big data analysis, blockchain data systems, robotics, and drones are transforming marketing. Marketing education has adapted over the last 120 years driven by changes in marketing technology that have helped shape the courses taught. Marketing educators are facing challenges in…

Data Mining (DM) is one of the most offered courses in data analytics education. However, the design and delivery of DM courses present a number of challenges and issues that stem from the DM's interdisciplinary nature and the industry expectations to generate a broader range of skills from the analytics programs. In this research, we identified…

The purpose of this research is to examine the research that has been done on MOOCs by applying data mining and analytic approaches and to depict the current state of MOOC research. The text mining revealed four broad themes: (I) MOOCs as a mainstreaming learning model in HE, (II) motivation and engagement issues in MOOCs, (III) assessment issues…

A prominent goal of colleges and universities today is to enact data-driven teaching and learning. Faculty clearly play a key role, and yet they tend to have limited time, a lack of training in assessment or education research, and few incentives for engaging in this work. We describe a framework designed to address the practical and cultural…