Enhancing Intercultural Competence: Can it be done without Studying Abroad?

Participation in intensive, immersive, service-learning study abroad programs with intentional intercultural activities embedded in the course curriculum has been shown to enhance cultural competence as measured via the Intercultural Development Inventory (IDI®) (Krishnan, Masters, Holgate, Wang & Calahan, 2017; Krishnan, Lin & Benson, 2020). The purpose of this study was to evaluate the relative impact of embedding intercultural learning activities on students’ intercultural competence when included in an on-campus course compared to a study abroad program. The intervention group consisted of 34 students enrolled in the on-campus course which incorporated intentional intercultural activities. Forty-one students who did not take the course comprised the control group. Comparison of the pre- and post-IDI® scores showed a significant increase in intercultural competence in the intervention group and no change in score in the control group participants. Qualitative data supported these findings. The increase in group mean score is slightly lower than group mean score increases in study-abroad students. Results indicate that incorporating intentional intercultural learning activities in an on-campus course can be an effective mechanism for students to enhance intercultural competence without travelling abroad

Predicting Student Success in a Self-Paced Mathematics MOOC

abstract: While predicting completion in Massive Open Online Courses (MOOCs) has been an active area of research in recent years, predicting completion in self-paced MOOCS, the fastest growing segment of open online courses, has largely been ignored. Using learning analytics and educational data mining techniques, this study examined data generated by over 4,600 individuals working in a self-paced, open enrollment college algebra MOOC over a period of eight months. Although just 4% of these students completed the course, models were developed that could predict correctly nearly 80% of the time which students would complete the course and which would not, based on each student’s first day of work in the online course. Logistic regression was used as the primary tool to predict completion and focused on variables associated with self-regulated learning (SRL) and demographic variables available from survey information gathered as students begin edX courses (the MOOC platform employed). The strongest SRL predictor was the amount of time students spent in the course on their first day. The number of math skills obtained the first day and the pace at which these skills were gained were also predictors, although pace was negatively correlated with completion. Prediction models using only SRL data obtained on the first day in the course correctly predicted course completion 70% of the time, whereas models based on first-day SRL and demographic data made correct predictions 79% of the time.Dissertation/ThesisDoctoral Dissertation Educational Technology 201

From procrastination to engagement? An experimental exploration of the effects of an adaptive virtual assistant on self-regulation in online learning

Compared to traditional classroom learning, success in online learning tends to depend more on the learner’s skill to self-regulate. Self-regulation is a complex meta-cognitive skill set that can be acquired. This study explores the effectiveness of a virtual learning assistant in terms of (a) developmental, (b) general compensatory, and (c) differential compensatory effects on learners’ self-regulatory skills in a sample of N = 157 online learners using an experimental intervention-control group design. Methods employed include behavioural trace data as well as self-reporting measures. Participants provided demographic information and responded to a 24-item self-regulation questionnaire and a 20-item personality trait questionnaire. Results indicate that the adaptive assistance did not lead to substantial developmental shifts as captured in learners’ perceived levels of self-regulation. However, various patterns of behavioural changes emerged in response to the intervention. This suggests that the virtual learning assistant has the potential to help online learners effectively compensate for deficits (in contrast to developmental shifts) in self-regulatory skills that might not yet have been developed

Design Variables for Self-Directed Learning in MOOC Environment

Massive Open Online Courses (MOOCs) can meet education needs from diverse social, cultural, and access backgrounds and require a minimal cost of resources from learners. To successfully scaffold large and distributed populations to learn effectively in these MOOCs, the design needs to optimize self-directed learning. In this paper, the researchers investigated the design variables for MOOCs\u27 learning environment that allowed learning choices made by learners. With this study, the researchers developed a 21-item questionnaire based on a review of the literature and their MOOC design and implementation practices, Massive Online Open Course Learning Environment Design Questionnaire (MOOC-LED). The researchers used the quantitative survey study and developed an initial examination of the MOOC-LED factor structure, validity, and internal reliability. The analyses were based on the anonymous data of 162 participants’ perception of learning in MOOCs. The scholarly significance of the 21-item MOOC-LED questionnaire is discussed with its limitations, implications, and future directions

Learning in an Introductory Physics MOOC: All Cohorts Learn Equally, Including an On-Campus Class

We studied student learning in the MOOC 8.MReV Mechanics ReView, run on the edX.org open source platform. We studied learning in two ways. We administered 13 conceptual questions both before and after instruction, analyzing the results using standard techniques for pre- and posttesting. We also analyzed each week’s homework and test questions in the MOOC, including the pre- and posttests, using item response theory (IRT). This determined both an average ability and a relative improvement in ability over the course. The pre- and posttesting showed substantial learning: The students had a normalized gain slightly higher than typical values for a traditional course, but significantly lower than typical values for courses using interactive engagement pedagogy. Importantly, both the normalized gain and the IRT analysis of pre- and posttests showed that learning was the same for different cohorts selected on various criteria: level of education, preparation in math and physics, and overall ability in the course. We found a small positive correlation between relative improvement and prior educational attainment. We also compared homework performance of MIT freshmen taking a reformed on-campus course with the 8.MReV students, finding them to be considerably less skillful than the 8.MReV students.Google (Firm) (Faculty Award)Massachusetts Institute of TechnologyNational Science Foundation (U.S.

How do we model learning at scale?:A systematic review of research on MOOCs

Despite a surge of empirical work on student participation in online learning environments, the causal links between the learning-related factors and processes with the desired learning outcomes remain unexplored. This study presents a systematic literature review of approaches to model learning in Massive Open Online Courses offering an analysis of learning-related constructs used in the prediction and measurement of student engagement and learning outcome. Based on our literature review, we identify current gaps in the research, including a lack of solid frameworks to explain learning in open online setting. Finally, we put forward a novel framework suitable for open online contexts based on a well-established model of student engagement. Our model is intended to guide future work studying the association between contextual factors (i.e., demographic, classroom, and individual needs), student engagement (i.e., academic, behavioral, cognitive, and affective engagement metrics), and learning outcomes (i.e., academic, social, and affective). The proposed model affords further interstudy comparisons as well as comparative studies with more traditional education models. </jats:p