Case Study Proposal: The Affordances of Digital Tools for the Self-Directed Learner


The Western Academy of Beijing (WAB) defines self-directed learning as ‘teaching students how to learn for themselves . . . [where] learners take responsibility to set goals, access resources and choose strategies for learning’ (2016, para. 2). To support the shift towards a culture of self-directed learning (SDL), high school students are provided with many opportunities for SDL experiences such as three 70-minute blocks of non-contract time in school each week. McLoughlin & Lee (2010) suggest learning technologies in conjunction with appropriate strategies afford greater agency to the student by allowing autonomy, a key characteristic of SDL (Du Toit-Brits & Van Zyl, 2017) through, for example, the promotion of social and participatory learning experiences and the use of rich digital media. WAB is technology-rich school where students and faculty have access to a wide range of digital tools and systems, many of which could or have already been purposely configured to support self-directed learning experiences.

Research Question

How, and to what extent, can digital tools support self-directed learning experiences in a high school?

Expected Outcomes

  1. Insight into the characteristics and attitudes of a self-directed learner (Du Toit-Brits & Van Zyl, 2017).
  2. An understanding of the types of digital tools, in conjunction with appropriate strategies, that support self directed learning experiences (McLoughlin & Lee, 2010; Robertson, 2011; Song & Hill, 2007) leading to the creation of a SDL Digital Tools & Strategies Map.
  3. Exploration of what and how digital tools currently support students involved in SDL at WAB.
  4. Insight into the challenges and barriers faced by students in using digital tools to support SDL in general and specifically at WAB (Lee, Tsai, Chai, & Koh, 2014).
  5. Recommendations to promote the use of digital tools at WAB, and potentially for high schools in general, to support self-directed learning that may include new tools, coaching and training for teachers and students, repurposing existing tools, training guides.

Proposed Research Plan

Dates Tasks Resources
Aug 11 Submit case study proposal submission

Continue literature review/scan to define self-directed learning and the general features, skills and/or characteristics.

Journals, blogs
Aug 20 Continue literature review/scan

Formulate draft of SDL definition and skills for SDL

Discuss with WAB curriculum leaders (f2f) and with PLN (online)

Refine SDL definition and skills.

Participatory research may include: use of Twitter for discussion, posting of blog post for feedback from PLN, face-to-face interviews.
Aug 27 Create SDL Digital Tool & Strategies Map: Use secondary research and consult with PLN to create map of types of digital tools and strategies to support skills for SDL

Compile list of digital tools at WAB that support SDL Digital Tool & Strategies Map.

Consult literature and PLN for SDL Digital Tool & Strategies Map.

At WAB: Interview(s) with eLearning team, IT support and other experts

Sep 3 Conduct interviews with 3-4 students to confirm/test WAB digital tool kit and to add further suggestions from students.

Analyse data to create a survey

Students TBD
Sep 10 – 17 Disseminate survey to student group and collect data Student group TBD

O365 forms

Sep 24 CHINA STUDIES WEEK – No students on campus
Oct 1 – 11 Analyse data, write up findings and after discussions with experts, determine the recommendations

Final edit and proofing of report

Oct 11 Submit Case Study Report


Du Toit-Brits, C., & Van Zyl, C.-M. (2017). Self-directed learning characteristics: making learning personal, empowering and successful. Africa Education Review, 1–20.

Lee, K., Tsai, P.-S., Chai, C. S., & Koh, J. H. L. (2014). Students’ perceptions of self-directed learning and collaborative learning with and without technology. Journal of Computer Assisted Learning, 30(5), 425–437.

McLoughlin, C., & Lee, M. J. W. (2010). Personalised and self-regulated learning in the Web2.0 era: International exemplars of innovative pedagogy using social software. Australasian Journal of Educational Technology, 26(1), 28–43.

Robertson, J. (2011). The educational affordances of blogs for self-directed learning. Computers and Education, 57(2), 1628–1644.

Song, L., & Hill, J. R. (2007). A Conceptual Model for Understanding Self-Directed Learning in Online Environments. Journal of Interactive Online Learning, 6(1), 27–42.

Western Academy of Beijing (Ed.). (2016). Targets. Retrieved August 10, 2017, from

Image from:

Embarking on a Case Study: Looking at Self-Directed Learning (SDL)

I am presenting a case study for my final assignment in my final subject in my MEd KNDI where I will be examining the extent to which our students entering the High School are prepared for self-directed learning (SDL). The following are some of my reflections as I start this journey.

Interpretation: an essential component of research

One of the first preparation readings provides good examples to show what research is and, more importantly, what it isn’t. I know that I need to learn more about SDL and as tempted as I am to suggest that a quick scan of journals and websites might be described as research, in reality this is just a quick dive or perhaps ‘information discovery’ (Leedy P. & Ormrod, 2013, p. 1). As I rummage around further in the readings to cobble together a better understanding of SDL, this is still not research but an ‘exercise in self-enlightenment’ (p. 2). Even when I pull out and begin to form a list of characteristic and features of a self-directed learner, this is still not research but perhaps more aptly described as ‘fact organisation’ (p. 2). According to Leedy & Ormrod (2013), the essence of research is the interpretation which is something I have yet to do with this data.

Cognation vs Metacognition

In my readings, I discover that both cognitive and metacognitive strategies are both important characteristics of SDL. What, therefore, is the difference between these two terms? A great example is given here on a website (Cognition vs. Metacognition) that does lack authority (see the citation below); however this example was somewhat supported after a another quick scan (Anderson, Betts, Ferris, & Fincham, 2011). A cognitive task may be to use find the sum of a set of numbers. A metacognitive task may be to add the numbers up again. The cognitive task is knowing how to reach the goal, in this case add up the numbers, whereas the metacognitive task is to check that the goal has been reached, in this case check the answer. Therefore, for my case study, I may need to gather data on both cognitive and metacognitive strategies which I will then need to analyse and interpret to determine the level of readiness for SDL.


Anderson, J. R., Betts, S., Ferris, J. L., & Fincham, J. M. (2011). Cognitive and Metacognitive Activity in Mathematical Problem Solving: Prefrontal and Parietal Patterns. Cognitive, Affective & Behavioral Neuroscience, 11(1), 52–67.

Cognition vs. Metacognition. (n.d.). Retrieved August 09, 2017, from

Leedy P., & Ormrod, J. (2013). The nature and tools of research. In Practical research : planning and design (pp. 1–26).

Featured Image:

This entry is an slightly edited version of the post in my CSU blog.