Cultivating learning and social interaction in an international classroom through small group work; a quasi-experimental study

Globalisation demands graduates to be culturally adept: cross-cultural experiences within an international classroom are an important part of contemporary higher education agendas (Kimmel & Volet, 2012; Montgomery, 2009; Rienties, Johan, & Jindal-Snape, 2014). The opportunities for learning from other cultures is noted as one of the reasons for international students studying abroad (Merrick, 2004). Patterson, Carrillo, and Salinas (2012) documented that cross-cultural learning could bring a number of advantages for both host-national and international students, such as understanding and appreciation of the world, ability to think critically, integrate multiple perspectives, acquiring global knowledge and hence to be able to work effectively in a global world. While studying abroad is increasingly common (Brisset, Safdar, Lewis, & Sabatier, 2010; Montgomery, 2009), research consistently suggests that international students continue to face a number of transitional challenges (Rienties, Beausaert, Grohnert, Niemantsverdriet, & Kommers, 2012; Ye, 2006; Zhou, Jindal-Snape, Topping, & Todman, 2008)

A multi-modal study into students’ timing and learning regulation: time is ticking

Purpose This empirical study aims to demonstrate how the combination of trace data derived from technology-enhanced learning environments and self-response survey data can contribute to the investigation of self-regulated learning processes. Design/methodology/approach Using a showcase based on 1,027 students’ learning in a blended introductory quantitative course, the authors analysed the learning regulation and especially the timing of learning by trace data. Next, the authors connected these learning patterns with self-reports based on multiple contemporary social-cognitive theories. Findings The authors found that several behavioural facets of maladaptive learning orientations, such as lack of regulation, self-sabotage or disengagement negatively impacted the amount of practising, as well as timely practising. On the adaptive side of learning dispositions, the picture was less clear. Where some adaptive dispositions, such as the willingness to invest efforts in learning and self-perceived planning skills, positively impacted learning regulation and timing of learning, other dispositions such as valuing school or academic buoyancy lacked the expected positive effects. Research limitations/implications Due to the blended design, there is a strong asymmetry between what one can observe on learning in both modes. Practical implications This study demonstrates that in a blended setup, one needs to distinguish the grand effect on learning from the partial effect on learning in the digital mode: the most adaptive students might be less dependent for their learning on the use of the digital learning mode. Originality/value The paper presents an application of embodied motivation in the context of blended learning

Toward Emotionally Accessible Massive Open Online Courses (MOOCs)

This paper outlines an approach to evaluating the emotional content of three Massive Open Online Courses (MOOCs) using the affective computing approach of prosody detection on two different text-to-speech voices in conjunction with human raters judging the emotional content of course text. The intent of this work is to establish the potential variation on the emotional delivery of MOOC material through synthetic voice

Linking students' timing of engagement to learning design and academic performance

In recent years, the connection between Learning Design (LD) and Learning Analytics (LA) has been emphasized by many scholars as it could enhance our interpretation of LA findings and translate them to meaningful interventions. Together with numerous conceptual studies, a gradual accumulation of empirical evidence has indicated a strong connection between how instructors design for learning and student behaviour. Nonetheless, students' timing of engagement and its relation to LD and academic performance have received limited attention. Therefore, this study investigates to what extent students' timing of engagement aligned with instructor learning design, and how engagement varied across different levels of performance. The analysis was conducted over 28 weeks using trace data, on 387 students, and replicated over two semesters in 2015 and 2016. Our findings revealed a mismatch between how instructors designed for learning and how students studied in reality. In most weeks, students spent less time studying the assigned materials on the VLE compared to the number of hours recommended by instructors. The timing of engagement also varied, from in advance to catching up patterns. High-performing students spent more time studying in advance, while low-performing students spent a higher proportion of their time on catching-up activities. This study reinforced the importance of pedagogical context to transform analytics into actionable insights

Regular Online Assessment, Motivation and Learning

In 2002 regular online assessment was introduced as one of the pillars of an improved course in economics for business students. These online tests were introduced in the context of the problem-based teaching format used at Universiteit Maastricht, where students work in small groups guided by tasks. In this student-centred approach it is important that students come well-prepared to their group meetings. For students this is a type of Prisoner’s Dilemma, because students can free-ride on the preparation of other students. It has also characteristics of an Assurance Game, because if a large part of the group is not well-prepared, the students that did prepare well will also get not much out of the group discussion and therefore will be less motivated to prepare for themselves, too. The risk that such an Assurance Game arises is higher when the majority of students is not intrinsically motivated at the start of the course. The interest in the subject matter of the course will certainly not increase when students do not study enough. Regular online assessment may help to solve these dilemmas by forcing students to prepare at least the textbook they have to read before the group meetings.In this paper we discuss the role of online testing in the context of problem-based learning and show that after the introduction of online learning and other innovations students worked harder, had the feeling that they learned more and reported to be more interested in the subject-matter of the course (i.e. economics). It is obvious that the increase in work effort and motivation as the consequence of online testing is not limited to the context of a problem-based learning environment.Economics ;

A review of ten years of implementation and research in aligning learning design with learning analytics at the Open University UK

There is an increased recognition that learning design drives both student learning experience and quality enhancements of teaching and learning. The Open University UK (OU) has been one of few institutions that have explicitly and systematically captured the designs for learning at a large scale. By applying advanced analytical techniques on large and fine-grained datasets, the OU has been unpacking the complexity of instructional practices, as well as providing conceptual and empirical evidence of how learning design influences student behaviour, satisfaction, and performance. This study discusses the implementation of learning design at the OU in the last ten years, and critically reviews empirical evidence from eight recent large-scale studies that have linked learning design with learning analytics. Four future research themes are identified to support future adoptions of learning design approaches

Stability and sensitivity of Learning Analytics based prediction models

Learning analytics seek to enhance the learning processes through systematic measurements of learning related data and to provide informative feedback to learners and educators. Track data from Learning Management Systems (LMS) constitute a main data source for learning analytics. This empirical contribution provides an application of Buckingham Shum and Deakin Crick’s theoretical framework of dispositional learning analytics: an infrastructure that combines learning dispositions data with data extracted from computer-assisted, formative assessments and LMSs. In two cohorts of a large introductory quantitative methods module, 2049 students were enrolled in a module based on principles of blended learning, combining face-to-face Problem-Based Learning sessions with e-tutorials. We investigated the predictive power of learning dispositions, outcomes of continuous formative assessments and other system generated data in modelling student performance and their potential to generate informative feedback. Using a dynamic, longitudinal perspective, computer-assisted formative assessments seem to be the best predictor for detecting underperforming students and academic performance, while basic LMS data did not substantially predict learning. If timely feedback is crucial, both use-intensity related track data from e-tutorial systems, and learning dispositions, are valuable sources for feedback generation

Discussion Analytics: Identifying Conversations and Social Learners in FutureLearn MOOCs

Discussion among learners in MOOCs has been hailed as beneficial for social constructive learning. To understand the pedagogical value of MOOC discussion forums, several researchers have utilized content analysis techniques to associate individual postings with differing levels of cognitive activity. However, this analysis typically ignores the turn taking among discussion postings, such as learners responding to others’ replies to their posts, learners receiving no reply for their posts, or learners just posting without conversing with others. This information is particularly important in understanding patterns of conversations that occur in MOOCs, and learners’ commenting behaviors. Therefore, in this paper we categorize comments in a FutureLearn MOOC based on their nature (post vs. reply to others’ post), classify learners based on their contributions for each type of post-ing, and identify conversations based on the types of comments composing them. This categorization quantifies the dynamics of conversations in the discussion activities, allowing monitoring of on-going discussion activities in FutureLearn and further analysis of identified conversations, social learners, and course steps with an unusually high number of a particular type of comment