How Students Learn in ChinaX: A Qualitative Investigative Interview Study

• In Event: Learning Beyond the Platform: Qualitative Studies in Massive Open Online Courses

Abstract

Objectives

ChinaX is a unique MOOC, lasting eighteen months across ten modules. For this paper, we examine how ChinaX students learn by investigating their study behaviors. Findings from these interviews can inform and guide the design of future MOOCs by improving student engagement. To this end, we conducted semi-structured interviews with 118 students to understand their experiences within ChinaX, including their motivation and persistence through ChinaX.

Perspectives/Theoretical Framework

One challenge faced when studying digital learning environments is the focus on log-data, which only tracks on-platform activity, thus failing to capture a large part of a student’s experiences. Although log-data is useful, it is unable to fully capture the learner’s motivations, values, learning, and impact of participating in MOOCs. To better understand the diversity of experiences among MOOC students, we need other research methods including connected ethnographies (Sefton-Green, 2012). To this end, we interviewed ChinaX participants about their experiences, focusing on their learning behaviors, as the lack of instructor oversight and feedback within MOOCs make effective self-regulation of learning critical (Bjork, Dunlosky, & Kornell, 2013).

Data Sources and Methods

In our end-of-the-course and our mid-course surveys, we asked students to participate in an interview of their experiences. Among 1,012 students who took the two surveys, 475 agreed to be interviewed, of which we interviewed 118. Each interview was 30-60 minutes and was transcribed by a professional transcription service and coded by the authors. After identifying major themes and reaching an acceptable interrater reliability score across the authors (κ = .71), we proceeded with a grounded theory model, focusing on these major themes to identify sub-codes within each.

Results

Preliminary analysis revealed that students showcased their diversity in how they accessed and learned course content. While computers were the most common medium for accessing content, students reported using tablets, smartphones, and even media-streaming players (e.g., Apple TV, Chromecast). Many also reported downloading and listening to the audio of the associated content, or reading the transcripts. Students furthered their learning off-platform in a variety of ways including sharing and discussing content on social media, finding additional readings, and researching course-related material. Students’ study behaviors changed and became more streamlined over the duration of the course. For example, several students reported transitioning away from traditional note-taking behaviors in favor of annotating the video transcripts or reviewing peers’ notes. Importantly, across all of the different behaviors reported within our interviews, many students indicated some metacognitive awareness about how they learned best. This finding suggests that successful MOOC students have sophisticated theories regarding their own learning, and will adapt their environment or behaviors to optimize their online learning experiences.

Significance

Findings from this study have theoretical and practical implications. Researchers who use log data need to be aware of common student behaviors that cannot be inferred by such data. Course developers can use these findings to guide course design and to better cultivate and optimize student study behaviors.

Authors

• Michael Friedman, Harvard University

• Meghan Morrissey, Harvard University

• Selen Turkay, Harvard University

• Tiffany Wong, Harvard University