Showing papers in "International Journal of Educational Technology in Higher Education in 2020"

Mapping research in student engagement and educational technology in higher education: a systematic evidence map

Abstract: Digital technology has become a central aspect of higher education, inherently affecting all aspects of the student experience. It has also been linked to an increase in behavioural, affective and cognitive student engagement, the facilitation of which is a central concern of educators. In order to delineate the complex nexus of technology and student engagement, this article systematically maps research from 243 studies published between 2007 and 2016. Research within the corpus was predominantly undertaken within the United States and the United Kingdom, with only limited research undertaken in the Global South, and largely focused on the fields of Arts & Humanities, Education, and Natural Sciences, Mathematics & Statistics. Studies most often used quantitative methods, followed by mixed methods, with little qualitative research methods employed. Few studies provided a definition of student engagement, and less than half were guided by a theoretical framework. The courses investigated used blended learning and text-based tools (e.g. discussion forums) most often, with undergraduate students as the primary target group. Stemming from the use of educational technology, behavioural engagement was by far the most often identified dimension, followed by affective and cognitive engagement. This mapping article provides the grounds for further exploration into discipline-specific use of technology to foster student engagement.

Predicting academic success in higher education: literature review and best practices

Abstract: Student success plays a vital role in educational institutions, as it is often used as a metric for the institution’s performance. Early detection of students at risk, along with preventive measures, can drastically improve their success. Lately, machine learning techniques have been extensively used for prediction purpose. While there is a plethora of success stories in the literature, these techniques are mainly accessible to “computer science”, or more precisely, “artificial intelligence” literate educators. Indeed, the effective and efficient application of data mining methods entail many decisions, ranging from how to define student’s success, through which student attributes to focus on, up to which machine learning method is more appropriate to the given problem. This study aims to provide a step-by-step set of guidelines for educators willing to apply data mining techniques to predict student success. For this, the literature has been reviewed, and the state-of-the-art has been compiled into a systematic process, where possible decisions and parameters are comprehensively covered and explained along with arguments. This study will provide to educators an easier access to data mining techniques, enabling all the potential of their application to the field of education.

Students’ perceptions on distance education: a multinational study

Abstract: Many universities offer Distance Education (DE) courses and programs to address the diverse educational needs of students and to stay current with advancing technology. Some Institutions of Higher Education (IHE) that do not offer DE find it difficult to navigate through the steps that are needed to provide such courses and programs. Investigating learners’ perceptions, attitudes and willingness to try DE can provide guidance and recommendations for IHEs that are considering expanding use of DE formats. A survey was distributed to undergraduate students in Portugal, UAE and Ukraine. The results of this pilot study showed that in all three countries, students’ major concerns about such programs were time management, motivation, and English language skills. Although students were somewhat apprehensive many indicated they were interested in taking DE courses. Six recommendations informed by interpretation of students’ responses and the literature, are offered to assist institutions who want to offer DE as part of their educational strategy.

Development of a new model on utilizing online learning platforms to improve students’ academic achievements and satisfaction

Abstract: This research aims to explore and investigate potential factors influencing students’ academic achievements and satisfaction with using online learning platforms. This study was constructed based on Transactional Distance Theory (TDT) and Bloom’s Taxonomy Theory (BTT). This study was conducted on 243 students using online learning platforms in higher education. This research utilized a quantitative research method. The model of this research illustrates eleven factors on using online learning platforms to improve students’ academic achievements and satisfaction. The findings showed that the students’ background, experience, collaborations, interactions, and autonomy positively affected students’ satisfaction. Moreover, effects of the students’ application, remembering, understanding, analyzing, and satisfaction was positively aligned with students’ academic achievements. Consequently, the empirical findings present a strong support to the integrative association between TDT and BTT theories in relation to using online learning platforms to improve students’ academic achievements and satisfaction, which could help decision makers in universities and higher education and colleges to plan, evaluate, and implement online learning platforms in their institutions.

Big data in education: a state of the art, limitations, and future research directions

Abstract: Big data is an essential aspect of innovation which has recently gained major attention from both academics and practitioners. Considering the importance of the education sector, the current tendency is moving towards examining the role of big data in this sector. So far, many studies have been conducted to comprehend the application of big data in different fields for various purposes. However, a comprehensive review is still lacking in big data in education. Thus, this study aims to conduct a systematic review on big data in education in order to explore the trends, classify the research themes, and highlight the limitations and provide possible future directions in the domain. Following a systematic review procedure, 40 primary studies published from 2014 to 2019 were utilized and related information extracted. The findings showed that there is an increase in the number of studies that address big data in education during the last 2 years. It has been found that the current studies covered four main research themes under big data in education, mainly, learner’s behavior and performance, modelling and educational data warehouse, improvement in the educational system, and integration of big data into the curriculum. Most of the big data educational researches have focused on learner’s behavior and performances. Moreover, this study highlights research limitations and portrays the future directions. This study provides a guideline for future studies and highlights new insights and directions for the successful utilization of big data in education.

University teachers' perception of barriers to the use of digital technologies: the importance of the academic discipline

Abstract: Digital technologies are currently one of the most used resources among students for developing their personalized learning environment. However, recent studies continue to demonstrate a lack of usage on the part of teaching staff for developing their teaching practices, especially at the university level. Through the identification of personal, professional, institutional, and contextual barriers, this study seeks to reveal the reasons why teachers in institutions of higher education do not use digital technologies for teaching purposes and whether the academic discipline influences this perception. The results suggest that professional barriers are the most prevalent and that the discipline of arts and humanities is where the most obstacles are perceived. In conclusion, there is a need for better professional development for teachers and more institutional involvement through strategic plans.

A balancing act: a window into online student engagement experiences

Abstract: This article reports on a qualitative study which explored online student engagement experiences in a higher education institution. There are very few studies providing in-depth perspectives on the engagement experiences of online students. The project adopted a case study approach, following 24 online students over one academic year. The setting for the study was an undergraduate online Humanities programme at Dublin City University. The research question for the study was: What themes are central to online student engagement experiences? Data was collected from participant-generated learning portfolios and semi-structured interviews and analysed following a data-led thematic approach. The five central themes that make up the study’s findings highlight key issues of students’ sense of community, their support networks, balancing study with life, confidence, and their learning approaches. The findings of this study indicate that successful online student engagement was influenced by a number of psychosocial factors such as peer community, an engaging online teacher, and confidence and by structural factors such as lifeload and course design. One limitation of the study is that it is a relatively small, qualitative study, its findings provide insights into how online degrees can support online students to achieve successful and engaging learning experiences.

Prerequisites for artificial intelligence in further education: identification of drivers, barriers, and business models of educational technology companies

Abstract: The ongoing datafication of our social reality has resulted in the emergence of new data-based business models. This development is also reflected in the education market. An increasing number of educational technology (EdTech) companies are entering the traditional education market with data-based teaching and learning solutions, and they are permanently transforming the market. However, despite the current market dynamics, there are hardly any business models that implement the possibilities of Learning Analytics (LA) and Artificial Intelligence (AI) to create adaptive teaching and learning paths. This paper focuses on EdTech companies and the drivers and barriers that currently affect data-based teaching and learning paths. The results show that LA especially are integrated into the current business models of EdTech companies on three levels, which are as follows: basic Learning Analytics, Learning Analytics and algorithmic or human-based recommendations, and Learning Analytics and adaptive teaching and learning (AI based). The discourse analysis reveals a diametrical relationship between the traditional educational ideal and the futuristic idea of education and knowledge transfer. While the desire for flexibility and individualization drives the debate on AI-based learning systems, a lack of data sovereignty, uncertainty and a lack of understanding of data are holding back the development and implementation of appropriate solutions at the same time.

Digital divide among higher education faculty

Abstract: Digital divide centers on access to various dimensions of information and communication technology (ICT) including physical access, motivation, skills, and actual usage of digital technologies. This divide tends to be even wider in the context of developing countries. Yet, there is a lack of literature on the digital divide among the faculty who teach in higher education settings. Thus, as a preliminary effort, by using a 57-item Faculty’s ICT Access (FICTA) scale, we investigated the digital inequalities (at the physical, motivational, skills, and usage levels) among Pakistani faculty in respect of their personal and positional categories. We also examined the relationship between faculty’s instructional usage of ICT and other dimensions of their ICT access. The findings revealed that there were significant differences in the faculty’s access to technology at the four levels in respect of their personal and positional categories. Further, the findings of the study shed light on the theoretical implications of the framework of successive kinds of ICT access suggested by van Dijk (The deepening divide: inequality in the information society, 2005).

Transitioning to the “new normal” of learning in unpredictable times: pedagogical practices and learning performance in fully online flipped classrooms

Abstract: The COVID-19 outbreak has compelled many universities to immediately switch to the online delivery of lessons. Many instructors, however, have found developing effective online lessons in a very short period of time very stressful and difficult. This study describes how we successfully addressed this crisis by transforming two conventional flipped classes into fully online flipped classes with the help of a cloud-based video conferencing app. As in a conventional flipped course, in a fully online flipped course students are encouraged to complete online pre-class work. But unlike in the conventional flipped approach, students do not subsequently meet face-to-face in physical classrooms, but rather online. This study examines the effect of fully online flipped classrooms on student learning performance in two stages. In Stage One, we explain how we drew on the 5E framework to design two conventional flipped classes. The 5E framework consists of five phases—Engage, Explore, Explain, Elaborate, and Evaluate. In Stage Two, we describe how we transformed the two conventional flipped classes into fully online flipped classes. Quantitative analyses of students’ final course marks reveal that the participants in the fully online flipped classes performed as effectively as participants in the conventional flipped learning classes. Our qualitative analyses of student and staff reflection data identify seven good practices for videoconferencing-assisted online flipped classrooms.

Examining learning engagement in MOOCs: a self-determination theoretical perspective using mixed method

Abstract: MOOCs as a learning approach are gaining popularity, and helping learners and instructors understand how learning engagement is constructed in a MOOC context is of increasing importance. Although previous research has undoubtedly enriched our knowledge of MOOCs, our understanding of student engagement in the MOOC context is still limited. This study adopts a sequential explanatory mixed-methods approach to examine student engagement in MOOCs from the self-determination theory (SDT) perspective. A total of 693 valid responses to a MOOC Engagement-Motivation scale were collected and 82 MOOC participants were interviewed. The results showed significant differences between the MOOC completers and non-completers with respect to the rank of motivators for enrolment and the rank of learning activities for participation. The association between perceived competence and emotional engagement was significantly higher in the MOOC completion group. The results of a multiple regression analysis indicated that the SDT model can significantly predict student engagement. Perceived competence registered the largest positive impact, and perceived relatedness had a slight negative impact on engagement. The three components of engagement can also predict learners’ perceived learning. Emotional engagement showed the largest positive impact. However, logistic regression analysis indicated that these components of engagement poorly predicted MOOC learners’ completion. Qualitative analyses of student interview data revealed three main factors that can promote learners’ SDT needs: active learning, course resources, and instructor accessibility. Implications of the findings can help MOOC designers and educators to better engage their participants.

Testing of support tools for plagiarism detection

Abstract: There is a general belief that software must be able to easily do things that humans find difficult. Since finding sources for plagiarism in a text is not an easy task, there is a wide-spread expectation that it must be simple for software to determine if a text is plagiarized or not. Software cannot determine plagiarism, but it can work as a support tool for identifying some text similarity that may constitute plagiarism. But how well do the various systems work? This paper reports on a collaborative test of 15 web-based text-matching systems that can be used when plagiarism is suspected. It was conducted by researchers from seven countries using test material in eight different languages, evaluating the effectiveness of the systems on single-source and multi-source documents. A usability examination was also performed. The sobering results show that although some systems can indeed help identify some plagiarized content, they clearly do not find all plagiarism and at times also identify non-plagiarized material as problematic.

Integrating students’ perspectives about online learning: a hierarchy of factors

Abstract: This article reports on a large-scale (n = 987), exploratory factor analysis study incorporating various concepts identified in the literature as critical success factors for online learning from the students’ perspective, and then determines their hierarchical significance. Seven factors--Basic Online Modality, Instructional Support, Teaching Presence, Cognitive Presence, Online Social Comfort, Online Interactive Modality, and Social Presence--were identified as significant and reliable. Regression analysis indicates the minimal factors for enrollment in future classes—when students consider convenience and scheduling—were Basic Online Modality, Cognitive Presence, and Online Social Comfort. Students who accepted or embraced online courses on their own merits wanted a minimum of Basic Online Modality, Teaching Presence, Cognitive Presence, Online Social Comfort, and Social Presence. Students, who preferred face-to-face classes and demanded a comparable experience, valued Online Interactive Modality and Instructional Support more highly. Recommendations for online course design, policy, and future research are provided.

Can artificial intelligence transform higher education

Abstract: * Correspondence: tony.bates@ubc.ca Chang School of Continuing Education, Ryerson University, Toronto, Canada Contact North, Greater Sudbury, Ontario, Canada Full list of author information is available at the end of the article The aim of this edition Many have argued that the development of artificial intelligence has more potential to change higher education than any other technological advance. For instance, Klutka et al. (2018) has listed the following goals for AI in higher education:

LMS-enabled blended learning utilization in distance tertiary education: establishing the relationships among facilitating conditions, voluntariness of use and use behaviour

Abstract: Distance education has evolved partly through technologies that defined them in the various generations of distance education delivery. However, in the twenty-first century, the use of Learning Management System (LMS) has changed the face of distance education delivery. Even the traditional face-to-face based distance education mode is now adopting the LMS as a mediating technology between instructors and students. However, in the usage of LMS-enabled blended learning, several factors have been cited in the literature as enablers towards actual usage of LMS technology. Factors such as facilitating conditions, voluntariness of use and actual use behaviour have been important in contemporary literature. Despite their importance, the chasm in the literature is the nuances existing in terms of relationships between these three factors. This study fills the gap by defining a model based on the three factors to provide an in depth empirical study on their relationships and how they influence LMS-enabled blended learning uptake of distance education by tutors. The study thus employs a cross country survey to collect data from 267 tutors and offer analysis by way of a Partial Least Squares Structural Equation Modelling (PLS-SEM) approach. The study revealed important relationships among facilitating conditions, voluntariness of use and use behaviour of LMS-enabled blended learning such that facilitating conditions predicted voluntariness of use and actual use behaviour; voluntariness of use determined actual LMS use behaviour for blended learning in distance education. The study finally provided recommendations based on the findings for policy and practice of LMS-enabled blended learning in distance education.

Improving online video lectures: learning challenges created by media

Abstract: The use of media within online university courses has been shown to aid the learning process with the delivery of information through various formats. However, issues that inhibit learning have been found when media are used inappropriately. Based on an examination of extant media research, this study arranges common media delivery problems into five main categories: pace, intelligibility, quality, media diversity, and congruence. Students taking online courses were asked to comment on the media used in their classes. Each of their comments was paired with one of the five categories with justification provided for the categorizations. Through analysis of these learner comments, this study examines the extent to which media delivery problems occur among a specific sample of online university learners in South Korea. The underlying pedagogy that explains the issues caused by inadequate delivery of media is also discussed, followed by recommended solutions that can address such pedagogical concerns. The results not only highlight existing solutions to these types of media problems but also build on them by suggesting other techniques in which media can be delivered so that the overall online learning experience may be enhanced.

Does sociability quality of web-based collaborative learning information system influence students’ satisfaction and system usage?

Abstract: The use of collaborative learning technologies is a stimulating element of collaborative learning process, where social interaction and collaboration are key factors. This research examines the impact of sociability quality on the usage of web-based collaborative learning information system (WBCLIS) and user satisfaction. We propose a theoretical model by integrating the construct of ‘sociability quality’ in the DeLone and McLean’s (Journal of Management Information Systems 19:9–30, 2003) updated information system success model. Proposed theoretical model was empirically validated, in a service-learning course with undergraduate students, where data was collected using an online questionnaire and evaluated through partial least square, structural equation modelling (PLS-SEM) statistical approach. Results suggest that, sociability quality has a direct positive impact on the system use and overall user satisfaction, along with a strong indirect impact on the net benefits of the WBCLIS. Findings also confirmed that, system use and user satisfaction are strong predictors of the net benefits. These results about sociability quality, contribute significantly in the domain of IS success literature, by identifying a novel and critical IS success dimension. Further, theoretical contribution in the context of sociability quality for IS success, and practical implications entailing the use of WBCLIS in the domain of service learning are also discussed.

Exploring the key influencing factors on college students’ computational thinking skills through flipped-classroom instruction

Abstract: To better understand students’ computational thinking skills (CTS) within the context of flipped-classroom instruction, a structural equation modeling analysis is employed to examine the key factors that influence student learning and students’ CTS when learning through flipped-classroom instruction. A total of 406 first-year college students responded to the survey. The results of this study show that student-to-student connectedness, learning motivation, and learning strategy have a direct impact on students’ CTS. In addition, indirect effects were found between student-to-student connectedness and CTS through learning motivation. Indirect effects were also found between learning motivation and CTS through the learning strategy in a flipped-classroom instruction situation. The findings of this research have practical implications for instructors, in that they should focus on the key factors that predict students’ computational thinking skills.

Teaching analytics, value and tools for teacher data literacy: a systematic and tripartite approach

Abstract: Teaching Analytics (TA) is a new theoretical approach, which combines teaching expertise, visual analytics and design-based research to support teacher’s diagnostic pedagogical ability to use data and evidence to improve the quality of teaching TA is now gaining prominence because it offers enormous opportunities to the teachers It also identifies optimal ways in which teaching performance can be enhanced Further, TA provides a platform for teachers to use data to reflect on teaching outcome The outcome of TA can be used to engage teachers in a meaningful dialogue to improve the quality of teaching Arguably, teachers need to develop their teacher data literacy and data inquiry skills to learn about teaching challenges These skills are dependent on understanding the connection between TA, LA and Learning Design (LD) Additionally, they need to understand how choices in particular pedagogues and the LD can enhance their teaching experience In other words, teachers need to equip themselves with the knowledge necessary to understand the complexity of teaching and the learning environment Providing teachers access to analytics associated with their teaching practice and learning outcome can improve the quality of teaching practice This research aims to explore current TA related discussions in the literature, to provide a generic conception of the meaning and value of TA The review was intended to inform the establishment of a framework describing the various aspects of TA and to develop a model that can enable us to gain more insights into how TA can help teachers improve teaching practices and learning outcome The Tripartite model was adopted to carry out a comprehensive, systematic and critical analysis of the literature of TA To understand the current state-of-the-art relating to TA, and the implications to the future, we reviewed published articles from the year 2012 to 2019 The results of this review have led to the development of a conceptual framework for TA and established the boundaries between TA and LA From the analysis the literature, we proposed a Teaching Outcome Model (TOM) as a theoretical lens to guide teachers and researchers to engage with data relating to teaching activities, to improve the quality of teaching

The benefits and caveats of using clickstream data to understand student self-regulatory behaviors: opening the black box of learning processes

Abstract: Student clickstream data—time-stamped records of click events in online courses—can provide fine-grained information about student learning. Such data enable researchers and instructors to collect information at scale about how each student navigates through and interacts with online education resources, potentially enabling objective and rich insight into the learning experience beyond self-reports and intermittent assessments. Yet, analyses of these data often require advanced analytic techniques, as they only provide a partial and noisy record of students’ actions. Consequently, these data are not always accessible or useful for course instructors and administrators. In this paper, we provide an overview of the use of clickstream data to define and identify behavioral patterns that are related to student learning outcomes. Through discussions of four studies, we provide examples of the complexities and particular considerations of using these data to examine student self-regulated learning.

Impact of students evaluation of teaching: a text analysis of the teachers qualities by gender

Abstract: Today, modern educational models are concerned with the development of the teacher-student experience and the potential opportunities it presents. User-centric analyses are useful both in terms of the socio-technical perspective on data usage within the educational domain and the positive impact that data-driven methods have. Moreover, the use of information and communication technologies (ICT) in education and process innovation has emerged due to the strategic perspectives and the process monitoring that have shown to be missing within the traditional education curricula. This study shows that there is an unprecedented increase in the amount of text-based data in different activities within the educational processes, which can be leveraged to provide useful strategic intelligence and improvement insights. Educators can apply the resultant methods and technologies, process innovations, and contextual-based information for ample support and monitoring of the teaching-learning processes and decision making. To this effect, this paper proposes an Educational Process and Data Mining (EPDM) model that leverages the perspectives or opinions of the students to provide useful information that can be used to enhance the end-to-end processes within the educational domain. Theoretically, this study applies the model to determine how the students evaluate their teachers by considering the gender of the teachers. We analyzed the underlying patterns and determined the emotional valence of the students based on their comments in the Students Evaluation of Teaching (SET). Thus, this work implements the proposed EPDM model using SET comments captured in a setting of higher education.

Simulation-based education involving online and on-campus models in different European universities

Abstract: Simulation-based education (SE) refers to the use of simulation software, tools, and serious games to enrich the teaching and learning processes. Advances in both computer hardware and software allow for employing innovative methodologies that make use of SE tools to enhance the learning experience. Moreover, thanks to the globalisation of e-learning practices, these educational experiences can be made available to students from different geographical regions and universities, which promotes the development of international and inter-university cooperation in education. This paper provides a review of recent works in the SE subject, with a focus on the areas of engineering, science, and management. It also discusses some experiences in SE involving different European universities and learning models. Finally, it also points out open challenges as well as noticeable trends.

Precision education with statistical learning and deep learning: a case study in Taiwan

Abstract: The low birth rate in Taiwan has led to a severe challenge for many universities to enroll a sufficient number of students. Consequently, a large number of students have been admitted to universities regardless of whether they have an aptitude for academic studies. Early diagnosis of students with a high dropout risk enables interventions to be provided early on, which can help these students to complete their studies, graduate, and enhance their future competitiveness in the workplace. Effective prelearning interventions are necessary, therefore students’ learning backgrounds should be thoroughly examined. This study investigated how big data and artificial intelligence can be used to help universities to more precisely understand student backgrounds, according to which corresponding interventions can be provided. For this study, 3552 students from a university in Taiwan were sampled. A statistical learning method and a machine learning method based on deep neural networks were used to predict their probability of dropping out. The results revealed that student academic performance (regarding the dynamics of class ranking percentage), student loan applications, the number of absences from school, and the number of alerted subjects successfully predicted whether or not students would drop out of university with an accuracy rate of 68% when the statistical learning method was employed, and 77% for the deep learning method, in the case of giving first priority to the high sensitivity in predicting dropouts. However, when the specificity metric was preferred, then the two approaches both reached more than 80% accuracy rates. These results may enable the university to provide interventions to students for assisting course selection and enhancing their competencies based on their aptitudes, potentially reducing the dropout rate and facilitating adaptive learning, thereby achieving a win-win situation for both the university and the students. This research offers a feasible direction for using artificial intelligence applications on the basis of a university’s institutional research database.

Virtual experimental platforms in chemistry laboratory education and its impact on experimental self-efficacy

Abstract: Self-efficacy is an important determinant in successfully attempting a task. In the area of education, self-efficacy plays a crucial role in causing behavioral changes, resulting in enhanced performance over the course of learning. In chemistry education, students often develop anxiety towards performing experiments due to the perceived negative outcomes resulting from lack of understanding and improper experimentation. This anxiety negatively impacts the self-efficacy of students in performing laboratory experiments. We define student-held beliefs about their experimental skills as ’experimental self-efficacy’ (ESE), and examine the four prominent factors that impact ESE in chemical laboratories. Through the development of an instrument, this work characterizes ESE and the impact of pre-laboratory interventions such as exposure to virtual laboratories (VL) on ESE and conceptual knowledge of students. Furthermore, analysis using statistical techniques such as t-tests and dissimilarity matrices reveal the positive impact of VL in enhancing students’ ESE.

Benefits of immersive collaborative learning in CAVE-based virtual reality

Abstract: How to make the learning of complex subjects engaging, motivating, and effective? The use of immersive virtual reality offers exciting, yet largely unexplored solutions to this problem. Taking neuroanatomy as an example of a visually and spatially complex subject, the present study investigated whether academic learning using a state-of-the-art Cave Automatic Virtual Environment (CAVE) yielded higher learning gains compared to conventional textbooks. The present study leveraged a combination of CAVE benefits including collaborative learning, rich spatial information, embodied interaction and gamification. Results indicated significantly higher learning gains after collaborative learning in the CAVE with large effect sizes compared to a textbook condition. Furthermore, low spatial ability learners benefitted most from the strong spatial cues provided by immersive virtual reality, effectively raising their performance to that of high spatial ability learners. The present study serves as a concrete example of the effective design and implementation of virtual reality in CAVE settings, demonstrating learning gains and thus opening opportunities to more pervasive use of immersive technologies for education. In addition, the study illustrates how immersive learning may provide novel scaffolds to increase performance in those who need it most.

Bringing technology to the mature classroom: age differences in use and attitudes

Abstract: Mature students are anecdotally thought to be more anxious about technology than younger students, to the extent that they avoid using technology. This is a problem in today’s higher education classrooms which often use a range of learning technologies, particularly as cohorts are becoming more and more likely to contain mature students. Previous work examining the attitudes of mature students to technology no longer reflects contemporary student age profiles or the current technological landscape. This study asks whether modern mature students in a UK university have more negative attitudes towards technology than younger students, and whether their usage of technology is different. A new diagnostic instrument, the Technology Attitudes Questionnaire, was developed to determine how students use technology for course activities and personal use, and their attitudes towards technology more generally. It was found that mature students use fewer technologies than younger students and use them less frequently, but have used them for a longer period over their lives. No difference was found for attitudes towards technology between the mature and younger groups. This research aims to contribute to the wider field of technology attitudes and use, particularly for the modern mature student cohort. These findings can be used to inform how educators design learning resources and use technology on their courses, working towards an age-inclusive programme.

Recommender systems to support learners’ Agency in a Learning Context: a systematic review

Abstract: Recommender systems for technology-enhanced learning are examined in relation to learners’ agency, that is, their ability to define and pursue learning goals. These systems make it easier for learners to access resources, including peers with whom to learn and experts from whom to learn. In this systematic review of the literature, we apply an Evidence for Policy and Practice Information (EPPI) approach to examine the context in which recommenders are used, the manners in which they are evaluated and the results of those evaluations. We use three databases (two in education and one in applied computer science) and retained articles published therein between 2008 and 2018. Fifty-six articles meeting the requirements for inclusion are analyzed to identify their approach (content-based, collaborative filtering, hybrid, other) and the experiment settings (accuracy, user satisfaction or learning performance), as well as to examine the results and the manner in which they were presented. The results of the majority of the experiments were positive. Finally, given the results introduced in this systematic review, we identify future research questions.

Teaching undergraduate mathematics fully online: a review from the perspective of communities of practice

Abstract: The use of fully online (FO) mathematics teaching has been increasing worldwide. Despite claims and findings that mathematics is more challenging to teach FO than face-to-face (F2F), we know little about FO mathematics teaching. In this paper, we address this gap by working to elucidate the differences between teaching in the FO and F2F modalities. We do this by examining FO and F2F teaching from the perspective of Communities of Practice (Wenger, Social learning systems and communities of practice, 2010) by comparing and contrasting current FO practices (or “ways of doing”) in the general undergraduate education community with current F2F practices in the undergraduate mathematics community. We identify six key differences between the two paradigms, which we recast to spotlight areas for technological and pedagogical development.

Features fostering academic and social integration in blended synchronous courses in graduate programs

Abstract: The purpose of this study was to examine the features that foster the academic and social integration of students enrolled in blended synchronous courses (BSC). Many studies and models have considered academic and social integration to be important determinants of student persistence and success in higher education programs and courses. In keeping with current research on blended courses that builds on models and theories developed for both online courses and face-to-face courses, we draw on Tinto’s model (Tinto, Review of Educational Research 45:89–125, 1975; Tinto, Leaving college: Rethinking the causes and cures of student attrition, 1993) and those of Rovai (The Internet & Higher Education 6:1–16, 2003) and Park (Proceedings of the 2007 Academy of Human Resource Development Annual Conference, 2007) to better define the academic and social integration of students in blended synchronous courses. To meet the study objective, a qualitative methodology was adopted. A convenience sampling technique was used in the study. The study participants were students (n = 8) enrolled in a graduate program in education offering only blended synchronous courses, as well as their instructors (n = 5). Semi-structured interviews (60–120 min in length) were selected as the data collection method. All qualitative data were analyzed using a general inductive approach (Thomas, American Journal of Evaluation 27:237–246, 2006). The results show that many features appear to promote academic and social integration, including the pedagogical strategies used. Moreover, this integration depends on the attitudes of both instructors and face-to-face students towards online students. This study highlights some challenges associated with blended synchronous courses. Further, it appears to suggest that instructors will need to work more on the inclusion of online students, and that training should be provided to assist them in this regard.

An academic Arabic corpus for plagiarism detection: design, construction and experimentation

Abstract: Advancement in information technology has resulted in massive textual material that is open to appropriation. Due to researchers’ misconduct, a plethora of plagiarism detection (PD) systems have been developed. However, most PD systems on the market do not support the Arabic language. In this paper, we discuss the design and construction of an Arabic PD reference corpus that is dedicated to academic language. It consists of (2312) dissertations that were defended by postgraduate students at the University of Jordan (JU) between the years 2001–2016. This Academic Jordan University Plagiarism Detection corpus; henceforth, JUPlag, follows the Dewey decimal classification (DDC) in the way it is structured. The goal of the corpus is twofold: Firstly, it is a database for the detection of plagiarism in student assignments, reports, and dissertations. Secondly, the n-gram structure of the corpus provides a knowledgebase for linguistic analysis, language teaching, and the learning of plagiarism-free writing. The PD system is guided by JU Library’s metadata for retrieval and discovery of plagiarism. To test JUPlag, we injected an unseen dissertation with multiple instances of plagiarism-simulated paragraphs and sentences. Experimentation with the system using different verbatim n-gram segments is indeed promising. Preliminary results encourage that permission be sought to enrich this corpus with all the theses in the Thesis Repository of the Union of Arab Universities. The JUPlag corpus is intended to function as an indispensable source for testing and evaluating plagiarism detection techniques. Since the University of Jordan is seeking to become a center for plagiarism detection for Arabic content and being a non-profit organization, it will charge a nominal fee for the use of JUPlag to finance the maintenance and development of the corpus.