Do professors matter?

Tomkin, Jonathan; Charlevoix, D. J.

doi:10.1145/2556325.2566245

Cited by 42 publications

(15 citation statements)

References 22 publications

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“…The positive emotions of those who successfully complete a program were also highlighted in the research of Milligan & Littlejohn (2014) and Kleiman, Wolf, & Frye (2015), due to the acquisition of knowledge, experience and professional development opportunities (Zutshi, O'Hare, & Rodafinos, 2013;Milligan & Littlejohn, 2014;Van Hentenryck & Coffrin, 2014;Kizilcec & Halawa, 2015;Park, Jung, & Reeves, 2015;Huang & Hew, 2016;Koutsodimou & Tzimogiannis, 2016) or simply because they achieved their goals (Beaven, Hauck, Comas-Quinn, Lewis, & de los Arcos, 2014;Wilkowski, Deutsch, & Russell, 2014;Kleiman et al, 2015;Li, 2015). In fact, the better the course design and the quality of the instructors and learning material (Oakley, Poole, & Nestor, 2016), the more satisfaction they feel and declare they are willing to attend other courses in the future (Belanger & Thornton, 2013;Tomkin & Charlevoix, 2014;Koutsodimou & Tzimogiannis, 2016), something that seems to happen in our research, too. Huang & Hew, 2016) (Min = 3.58, Max = 3.77, M = 3.69), but also the research of Li (2015) and Gnostopoulou (2018).…”

Section: Research Questionmentioning

confidence: 62%

The Influence of Instructional Design and Instructional Material on Learners’ Motivation and Completion Rates of a MOOC Course

Giasiranis¹,

Sofos²

2020

JSS

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Despite the formal, non-formal and informal training opportunities offered by MOOCs, they show low completion rates due to both personal factors of the participants and the instructional design of the courses. Empirical research has shown that good instructional design can help increase learners' motivation, helping them achieve high performance by completing the program. To determine to which extent the quality instructional material, which is the result of a good instructional design, contributes to their motivation and high rates of completion of the courses, we created an eight (8) week MOOC program on "Violence and bullying in schools", following the instructional design model "Systems Approach Model". The motivation of the learners from the instructional material was measured using the Instructional Material Motivation Survey (IMMS) tool and showed that all the learners were highly motivated both as a whole and in every factor of the ARCS model (Attention, Relevance, Confidence, Satisfaction) contributing to very high completion rates, as from the 1309 learners who started the program, 1050 completed it (80.2%). Considering the results of our research, we, finally, suggest some points that can be taken into account when designing similar programs.

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Section: Research Questionmentioning

confidence: 62%

The Influence of Instructional Design and Instructional Material on Learners’ Motivation and Completion Rates of a MOOC Course

Giasiranis¹,

Sofos²

2020

JSS

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“…It can be difficult to demonstrate measurable learning effects using design interventions in online courses. For example, while it is possible to increase student involvement in forums [1], improving grades and retention has remained challenging [8,55]. However, Talkabout increases both learning and engagement (Table 3).…”

Section: Discussionmentioning

confidence: 99%

“…Quizzes are a high-effort activity that most MOOC learners don't participate in: only 22.8% of students who watched a lecture video also participated in a quiz. This makes quizzes suitable as a high-effort engagement measure [8,55].…”

Section: Hypotheses and Measuresmentioning

confidence: 99%

Talkabout

Kulkarni

Cambre

Kotturi

et al. 2015

Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work &Amp; Social Computing

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Massive online classes are global and diverse. How can we harness this diversity to improve engagement and learning? Currently, though enrollments are high, students' interactions with each other are minimal: most are alone together. This isolation is particularly disappointing given that a global community is a major draw of online classes. This paper illustrates the potential of leveraging geographic diversity in massive online classes. We connect students from around the world through small-group video discussions. Our peer discussion system, Talkabout, has connected over 5,000 students in fourteen online classes. Three studies with 2,670 students from two classes found that globally diverse discussions boost student performance and engagement: the more geographically diverse the discussion group, the better the students performed on later quizzes. Through this work, we challenge the view that online classes are useful only when in-person classes are unavailable. Instead, we demonstrate how diverse online classrooms can create benefits that are largely unavailable in a traditional classroom.

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“…However, new possibilities emerge in the era of big educational data. For instance, in the case of N = all, experimental conditions can be engineered into everyday practice in the form of A/B studies (Tomkin & Charlevoix, 2014), where “all” is divided into Group A (users working in a beta version that includes the proposed software revisions) and Group B (current software version) in order to compare the effects of the changes created in the A software instantiation before full implementation. This is, in fact, how user testing occurs in “agile” software development methodologies (Martin, 2009).…”

Section: Toward Education Data Sciencementioning

confidence: 99%

Big Data Comes to School

Cope

Kalantzis

2016

AERA Open

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The prospect of “big data” at once evokes optimistic views of an information-rich future and concerns about surveillance that adversely impacts our personal and private lives. This overview article explores the implications of big data in education, focusing by way of example on data generated by student writing. We have chosen writing because it presents particular complexities, highlighting the range of processes for collecting and interpreting evidence of learning in the era of computer-mediated instruction and assessment as well as the challenges. Writing is significant not only because it is central to the core subject area of literacy; it is also an ideal medium for the representation of deep disciplinary knowledge across a number of subject areas. After defining what big data entails in education, we map emerging sources of evidence of learning that separately and together have the potential to generate unprecedented amounts of data: machine assessments, structured data embedded in learning, and unstructured data collected incidental to learning activity. Our case is that these emerging sources of evidence of learning have significant implications for the traditional relationships between assessment and instruction. Moreover, for educational researchers, these data are in some senses quite different from traditional evidentiary sources, and this raises a number of methodological questions. The final part of the article discusses implications for practice in an emerging field of education data science, including publication of data, data standards, and research ethics.

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Do professors matter?

Cited by 42 publications

References 22 publications

The Influence of Instructional Design and Instructional Material on Learners’ Motivation and Completion Rates of a MOOC Course

The Influence of Instructional Design and Instructional Material on Learners’ Motivation and Completion Rates of a MOOC Course

Talkabout

Big Data Comes to School

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