According to various international reports, Artificial Intelligence in Education (AIEd) is one of the currently emerging fields in educational technology. Whilst it has been around for about 30 years, it is still unclear for educators how to make pedagogical advantage of it on a broader scale, and how it can actually impact meaningfully on teaching and learning in higher education. This paper seeks to provide an overview of research on AI applications in higher education through a systematic review. Out of 2656 initially identified publications for the period between 2007 and 2018, 146 articles were included for final synthesis, according to explicit inclusion and exclusion criteria. The descriptive results show that most of the disciplines involved in AIEd papers come from Computer Science and STEM, and that quantitative methods were the most frequently used in empirical studies. The synthesis of results presents four areas of AIEd applications in academic support services, and institutional and administrative services: 1. profiling and prediction, 2. assessment and evaluation, 3. adaptive systems and personalisation, and 4. intelligent tutoring systems. The conclusions reflect on the almost lack of critical reflection of challenges and risks of AIEd, the weak connection to theoretical pedagogical perspectives, and the need for further exploration of ethical and educational approaches in the application of AIEd in higher education.

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Systematic review of research on artificial intelligence applications in higher education – where are the educators?

Studies have been conducted on university students’ continuous intention to learn online from the perspectives of learning motivation and capability, perceptions or attitudes, and online learning experiences. However, few have examined how the above factors will relate to each other and contribute to students’ online learning intention. This research explored 94 university students’ online learning attitudes and experiences in a blended course. The researchers investigated the changes in the participants’ attitudes toward online learning and the relationships between their self-regulated learning capability, online interactions, attitudes, and online learning intention. These students participated in a pre- and post-survey at the beginning and end of the course. They also completed six weekly reports commenting on their learning activities of the week. At the end of the course, interviews were administered to eight participants to gather detailed information about their online learning experiences. It was found that (a) the participants’ online learning attitudes were generally positive and increased when completing the course; and (b) the participants’ continuous intention to learn online was significantly predicted by four self-regulatory factors and attitudes, mediated through perceived online social interactions. The analysis of the interviewees’ further comments provided more insights about the potential factors contributing to their online learning attitude changes. The strategies for future online course design with a view of improving students’ self-regulated learning skills are discussed in this paper.

University students’ online learning attitudes and continuous intention to undertake online courses: a self-regulated learning perspective

The emergence of personalised data technologies such as learning analytics is framed as a solution to manage the needs of higher education student populations that are growing ever more diverse and...

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Empowering learners with personalised learning approaches? Agency, equity and transparency in the context of learning analytics

The significance of scaffolding in education has received considerable attention. Many studies have examined the effects of scaffolding with diverse groups of participants, purposes, learning outcomes, and learning environments. The purpose of this research was to conduct a meta-analysis of the effects of scaffolding on learning outcomes in an online learning environment in higher education. This meta-analysis included studies with 64 effect sizes from 18 journal articles published in English, in eight countries, from 2010 to 2019. The meta-analysis revealed that scaffolding in an online learning environment has a large and statistically significant effect on learning outcomes. The meta-cognitive domain yielded a larger effect size than did the affective and cognitive domains. In terms of types of scaffolding activities, meta-cognitive scaffolding outnumbered other types of scaffolding. Computers as a scaffolding source in an online learning environment were also more prevalent than were human instructors. In addition, scholars in the United States have produced a large portion of the scaffolding research. Finally, the academic area of language and literature has adopted scaffolding most widely. Given that effective scaffolding can improve the quality of learning in an online environment, the current research is expected to contribute to online learning outcomes and learning experiences.

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A Meta-Analysis of Scaffolding Effects in Online Learning in Higher Education

E-learning courses have become popular means of delivering knowledge to students in higher education institutions. Most participants of learning process note that they benefit from the possibility to gain knowledge regardless of time, location and device they use. Among other advantages possibility to return to learnt material several times, divide material into parts, consume information through different types of educational materials (video, infographics, presentations, text, quizzes etc.) are mentioned. At the same time most of the surveyed students chose that they lack personalization both of materials and studying process, limited in terms of fulfillment and would like to have a choice of the level of study. The educational trend that is able to put into practice the above mentioned requirements is blended learning as it has a range of advantages such as usability, consideration of individual abilities, additional materials introduction, activities monitoring. Although it combines offline and online learning, effectiveness of e-learning courses designed for its implementation play a crucial role. To make a learning process correspondent to the students’ needs adaptive learning can be introduced in higher education institutions. Adaptive learning is a methodology that allows to identify level of students’ knowledge and their learning styles and transform materials, tasks and ways of their delivery according to the needs of learning process participants. LMS Moodle offers different solutions for adaptive learning. They provide administrators and teachers with tools to vary all stages of a learning process starting with delivery of information and ending with assessment.

https://iopscience.iop.org/article/10.1088/1742-6596/1840/1/012062/pdf

Implementation of adaptive learning at higher education institutions by means of Moodle LMS

What is on the five-year horizon for higher education institutions? Which trends and technology developments will drive educational change? What are the critical challenges and how can we strategize solutions? These questions regarding technology adoption and educational change steered the discussions of 71 experts to produce this report. The six key trends, six significant challenges, and six developments in educational technology profiled in the report are likely to impact teaching, learning, and creative inquiry in higher education. The three sections of the report constitute a reference and technology planning guide for educators, higher education leaders, administrators, policymakers, and technologists.

Kristi DePaul

Papers

NMC Horizon Report: 2018 Higher Education Edition.

Horizon Report 2018 Higher Education Edition Brought to you by EDUCAUSE