Exactly how is personalization operationalized with the design & delivery of open courses?

A study was conducted to better understand how massive open online courses (MOOC) instructors adapt their courses to enhance or personalize MOOC design and delivery. This study explored the activities, tools, and resources that instructors of MOOCs used to improve the personalization of their MOOCs. Following email interviews with 22 MOOC and open education leaders, regarding MOOC personalization, a questionnaire was developed and completed by 152 MOOC instructors from around the world. While more than 8 in 10 respondents claimed heavy involved in designing their MOOCs, only one-third placed extensive effort on meeting unique learner needs during the actual design of that course and even fewer were concerned with personalization during the delivery of it. An array of instructional practices, technology tools, and content resources were leveraged by instructors to personalize MOOC-based learning environments. Aligning with previous research, the chief resources and tools employed in their MOOCs were discussion forums, video lectures, supplemental readings, and practice quizzes. Additionally, self-monitoring and peer-based methods of learner feedback were more common than instructor monitoring and/or feedback. Some respondents mentioned the use of flexible deadlines, proposed alternatives to course assignments, and introduced multimedia elements, mobile applications, and guest speakers among the ways in which they personalized their massive courses. A majority of the respondents reported modest or high interest in learning new techniques to personalize their next MOOC offering.

Keywords: massive open online courses (MOOCs), personalization, instructional design, open course, instructors

Interested in learning more? Check out the forthcoming publication.

Bonk, C. J., Zhu, M., Kim, M., Xu, S., Sabir, N., & Sari, A. (in press). Pushing toward a more personalized MOOC: Exploring instructor selected activities, resources, and technologies for MOOC design and implementation. The International Review of Research on Open and Distributed Learning (IRRODL).

Abstract adapted from article

 

Pictures are worth a 1000 words, but you don’t get that many when coding.

Mitchell (2011) claims that there is no roadmap when trekking through visual data, and there is no set way to engage in fieldwork or analyzing multimedia information (Pink, 2007).  Through the course of my development as a budding researcher I have often felt lost when trying to carry out carefully constructed research projects. Things don’t always seem to go as planned, and interpretations of procedures and information can sometimes become muddled.

In an attempt to explore how to best carry out such tasks as interpreting visual data, engaging in field work, and analyzing information I have turned to several texts and scholars.

One of the first courses, taken during my doctoral studies was grounded in evaluation research and needs analysis.  My journey through the semester allowed our research team to work with a client to establish goals that needed to be investigated. Surprisingly this course was very much a cookbook style that prescribed specific steps and assessments be taken in precise order.

Interestingly enough, none of my following methods courses truly provided the same structure.  During one qualitative inquiry course we worked through the Merriam (2009) and Seidman (2013) texts.  While these are great resources that provide starting points they provided too much variety in approaches.  It seemed that every unique project had a different approach to engaging in field work and analyzing information.  To further explore qualitative research designs, methods, and approaches, several students turned to Creswell’s (2012; 2013) texts.  While these cookbook resources are a great base to understand and compare qualitative, and mixed method, approaches to fieldwork, they don’t cover too many approaches to visual data analysis.

While text such as Emmison’s (2010) chapter on visual data offer great insight into the history of visual data and analysis approaches, they could be improved upon by commenting on best practices, and providing guidance for budding researchers. Other book chapters (i.e. Cohen, Manion & Morrison, 2011) provide an introductory discussion about visual data interpretation; but the larger lesson is that ‘it depends’ on the research questions and context.

 We seem to hear, “it depends” a lot in our field, no?

 

References

Cohen, L., Manion, L., & Morrison, K. (2011). Visual media in educational research. In Research Methods in Education (7th ed., pp. 526-534). New York, NY: Routledge.

Creswell, J. W. (2012). Qualitative inquiry and research design: Choosing among five approaches. Thousand Oaks, CA: Sage.

Creswell, J. W. (2013). Research design: Qualitative, quantitative, and mixed methods approaches. Thousand Oaks, CA: Sage.

Emmison, M. (2010). Conceptualizing visual data. In D. Silverman (Ed.), Qualitative Research (3rd ed., pp. 233-249). San Francisco, CA: Sage.

Merriam, S. B. (2009). Qualitative research: A guide to design and implementation. San Francisco, CA: John Wiley & Sons.

Mitchell, C. (2011). Doing visual research. London, UK: Sage.

Pink, S. (2013). Doing visual ethnography. London, UK: Sage.

Seidman, I. (2012). Interviewing as qualitative research: A guide for researchers in education and the social sciences. New York, NY: Teachers college press.