Giving Flexible Learners a Head Start on Higher Education: Designing and Implementing a Pre-induction Socialisation MOOC

Brunton, James; Brown, Mark; Costello, Eamon; Farrell, Orna; Mahon, Conor P.

doi:10.1007/978-3-319-59044-8_2

Cited by 8 publications

(11 citation statements)

References 7 publications

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“…We found several examples of skill outcomes, including self-assessed preparedness (readiness for online education) [ 37 ] and improvement in Spanish language pronunciation and comprehensibility measured by pre-MOOC and post-MOOC assessments [ 38 ]. Further, participants in a library and information science MOOC were asked “What did you gain most from taking part in the MOOC?” Their responses included “Students gained new technological skills through their learning experience ” [ 39 ].…”

Section: Resultsmentioning

confidence: 99%

Benefits of Massive Open Online Course Participation: Deductive Thematic Analysis

Blum¹,

Stenfors²,

Palmgren³

2020

J Med Internet Res

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Background Massive open online courses (MOOCs), as originally conceived, promised to provide educational access to anyone with an internet connection. However, the expansiveness of MOOC education has been found to be somewhat limited. Nonetheless, leading universities continue to offer MOOCs, including many in the health sciences, on a number of private platforms. Therefore, research on online education must include thorough understanding of the role of MOOCs. To date, studies on MOOC participants have focused mainly on learners’ assessment of the course. It is known that MOOCs are not reaching the universal audiences that were predicted, and much knowledge has been gained about learners’ perceptions of MOOCs. However, there is little scholarship on what learners themselves gain from participating in MOOCs. Objective As MOOC development persists and expands, scholars and developers should be made aware of the role of MOOCs in education by examining what these courses do offer their participants. The objective of this qualitative synthesis of a set of MOOC evaluation studies was to explore outcomes for MOOC learners, that is, how the learners themselves benefit from participating in MOOCs. Methods To explore MOOC learners’ outcomes, we conducted a qualitative synthesis in the form of a deductive thematic analysis, aggregating findings from 17 individual studies selected from an existing systematic review of MOOC evaluation methods. We structured our inquiry using the Kirkpatrick model, considering Kirkpatrick levels 2, 3, and 4 as potential themes in our analysis. Results Our analysis identified six types of Kirkpatrick outcomes in 17 studies. Five of these outcomes (learning/general knowledge, skills, attitudes, confidence, and commitment) fit into Kirkpatrick Level 2, while Kirkpatrick Level 3 outcomes concerning behavior/application were seen in four studies. Two additional themes were identified outside of the Kirkpatrick framework: culture and identity outcomes and affective/emotional outcomes. Kirkpatrick Level 4 was not represented among the outcomes we examined. Conclusions Our findings point to some gains from MOOCs. While we can expect MOOCs to persist, how learners benefit from the experience of participating in MOOCs remains unclear.

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Section: Resultsmentioning

confidence: 99%

Benefits of Massive Open Online Course Participation: Deductive Thematic Analysis

Blum¹,

Stenfors²,

Palmgren³

2020

J Med Internet Res

View full text Add to dashboard Cite

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“…In all, 12 studies used 1 data source [20,24,25,27,28,31,32,38,43,47], 11 studies used 2 data sources [21,26,29,30,33,34,36,40,42,45,51], 7 studies used 3 data sources [22,35,39,41,44,46,48], 2 studies used 4 data sources [23,37], and 1 study used 5 data sources [52]. The most used data sources were surveys followed by learning management system (LMS), quizzes, and interviews (Table 2).…”

Section: Massive Open Online Course Evaluation Data Collection Methodsmentioning

confidence: 99%

“…In all, 18 studies that collected data through the LMS [22,24,26,[29][30][31][33][34][35][39][40][41][42][44][45][46]48,52] had a population size made of participants or data points (eg, discussion posts) ranging from 59 to 209,871. Nine studies used quiz data [20][21][22]26,33,35,41,47,52]. Studies that used quiz data had a population size of 48 [20], 53 [47], 136 [41], 1080 [21], and [22].…”

Section: Massive Open Online Course Evaluation Data Collection Methodsmentioning

confidence: 99%

Massive Open Online Course Evaluation Methods: Systematic Review

Alturkistani¹,

Lam²,

Foley³

et al. 2020

J Med Internet Res

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Background Massive open online courses (MOOCs) have the potential to make a broader educational impact because many learners undertake these courses. Despite their reach, there is a lack of knowledge about which methods are used for evaluating these courses. Objective The aim of this review was to identify current MOOC evaluation methods to inform future study designs. Methods We systematically searched the following databases for studies published from January 2008 to October 2018: (1) Scopus, (2) Education Resources Information Center, (3) IEEE (Institute of Electrical and Electronic Engineers) Xplore, (4) PubMed, (5) Web of Science, (6) British Education Index, and (7) Google Scholar search engine. Two reviewers independently screened the abstracts and titles of the studies. Published studies in the English language that evaluated MOOCs were included. The study design of the evaluations, the underlying motivation for the evaluation studies, data collection, and data analysis methods were quantitatively and qualitatively analyzed. The quality of the included studies was appraised using the Cochrane Collaboration Risk of Bias Tool for randomized controlled trials (RCTs) and the National Institutes of Health—National Heart, Lung, and Blood Institute quality assessment tool for cohort observational studies and for before-after (pre-post) studies with no control group. Results The initial search resulted in 3275 studies, and 33 eligible studies were included in this review. In total, 16 studies used a quantitative study design, 11 used a qualitative design, and 6 used a mixed methods study design. In all, 16 studies evaluated learner characteristics and behavior, and 20 studies evaluated learning outcomes and experiences. A total of 12 studies used 1 data source, 11 used 2 data sources, 7 used 3 data sources, 4 used 2 data sources, and 1 used 5 data sources. Overall, 3 studies used more than 3 data sources in their evaluation. In terms of the data analysis methods, quantitative methods were most prominent with descriptive and inferential statistics, which were the top 2 preferred methods. In all, 26 studies with a cross-sectional design had a low-quality assessment, whereas RCTs and quasi-experimental studies received a high-quality assessment. Conclusions The MOOC evaluation data collection and data analysis methods should be determined carefully on the basis of the aim of the evaluation. The MOOC evaluations are subject to bias, which could be reduced using pre-MOOC measures for comparison or by controlling for confounding variables. Future MOOC evaluations should consider using more diverse data sources and data analysis methods. International Registered Report Identifier (IRRID) RR2-10.2196/12087

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“…Jones (2008) found that students can feel stronger commitment to their courses and are more likely to persist if there is a strong studentinstitution and programme match, good preparation prior to entry, minimal financial issues or personal difficulties, a satisfactory academic experience, and opportunities for meaningful social integration in the early stages of the academic year. At the institutional level, there should be high-level responsibility for nurturing a culture of belonging and creating the necessary infrastructure to promote student engagement, retention and success (Brunton et al 2017;Thomas 2012;Thomas et al 2017).…”

Section: Strategies To Promote Student Successmentioning

confidence: 99%

‘If I had missed it I would have been the lost little sheep’: Exploring student narratives on orientation to first year

Farrell

Brunton

Trevaskis

2019

Journal of Further and Higher Education

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include the psychology of identity formation, socialisation/induction processes for 'off-campus' higher education students, and digital assessment. Samantha Trevaskis is a third year undergraduate student studying Psychology with DCU Connected. Her research interests include Educational and Health psychology, as well as Neuropsychology. Upon completion of her undergraduate degree, Samantha will continue with further Education in Psychology. Induction by Design: Strategically supporting student transition into higher education This qualitative study reports on an on-entry induction and socialisation intervention designed to support and retain students transitioning to higher education at an Irish private higher education institution. Following the intervention, a qualitative study was conducted with the aim of exploring new undergraduate student experiences of transitioning to higher education and their experiences of participating in the induction by design. Six themes emerged during the analytical process: academic experiences, expectations of higher education, the impact of college on life, induction by design, socialisation and the transition from school to college. Findings indicate that designed induction interventions which focus on the on-entry phase of the study lifecycle can have a positive impact on new higher education students.

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Giving Flexible Learners a Head Start on Higher Education: Designing and Implementing a Pre-induction Socialisation MOOC

Cited by 8 publications

References 7 publications

Benefits of Massive Open Online Course Participation: Deductive Thematic Analysis

Benefits of Massive Open Online Course Participation: Deductive Thematic Analysis

Massive Open Online Course Evaluation Methods: Systematic Review

‘If I had missed it I would have been the lost little sheep’: Exploring student narratives on orientation to first year

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