Active and Data-Driven Learning: Approaches for Responsive Teaching

by Daron Benjamin Loo


Broad Context 

Active learning, according to Bonwell and Eison (1991), is a process that “involves students in doing things and thinking about the things they are doing.” (p. 2). Active learning in higher education is a research interest for scholars in the area of SoTL. Active learning may be encouraged through the flipped classroom approach (Hung, 2015), blended learning (Yang, Chaung, & Tseng, 2013), or by partnering with students in knowledge inquiry (Felten, 2013; Bryson, 2016). In the area of language education or applied linguistics, active learning is also a common interest (e.g. Lucas, 2008; Agbatogun, 2014; Hung, 2015). Active learning (and its many related terms) is relevant to today’s higher education context, especially when we consider the problem of access and excess of information on the Internet (see Dalton, 2012).


Situated Context

In my classroom setting (ES 5000), active learning is pivotal, since students will need to be able to interact with the teaching materials in order to improve their writing skills.



However, with the proliferation of information through the Internet, one issue to consider, then, is the way in which I present lessons. These lessons need to be novel and should not be easily replicated and found in online sources.


Potential Solution 

To address this issue, a teaching practice that I had integrated last year and in my current teaching is active learning. Specifically, a data-driven teaching/learning approach. Simply put, this approach supports active learning among students through the examination of authentic and relevant texts (see Boulton, 2010). An example of an activity that we do in class is the analysis of published research papers. Since it is a module that focuses on improving students’ basic grasp of English, the activities typically revolve around micro-analysis of discrete items such as grammar forms, syntax structure, and word usage to macro-analysis of bigger features such as organizational style and cohesion links. Texts that are analyzed are typically those of the students’ own choosing, or texts which can also be resources to aid students with their English language development (e.g. texts on language learning strategies, intercultural adjustment and communication, etc.).

I think that data-driven learning addresses the issue of presenting lessons in a useful and novel format. It also supports the inclusion of students in the learning process, which is a fundamental SoTL principle as suggested by Felten (2013), wherein “good (SoTL) practice requires students in the inquiry process.” (p. 123).



Agbatogun, A. O. (2014). Developing learners’ second language communicative competence through active learning: clickers or communicative approach? Journal of Educational Technology & Society17(2).

Boulton, A. (2010). Data‐driven learning: Taking the computer out of the equation. Language learning60(3), 534-572.

Bryson, C. (2016). Engagement through partnership: Students as partners in learning and teaching in higher education.

Dalton, K. M. (2012). Bridging the Digital Divide and Guiding the Millennial Generation’s Research and Analysis. Barry L. Rev.18, 167.

Felten, P. (2013). Principles of good practice in SoTL. Teaching and Learning Inquiry: The ISSOTL Journal1(1), 121-125.

Hung, H. T. (2015). Flipping the classroom for English language learners to foster active learning. Computer Assisted Language Learning28(1), 81-96.

Lucas, T., Villegas, A. M., & Freedson-Gonzalez, M. (2008). Linguistically responsive teacher education: Preparing classroom teachers to teach English language learners. Journal of Teacher Education59(4), 361-373.

Yang, Y. T. C., Chuang, Y. C., Li, L. Y., & Tseng, S. S. (2013). A blended learning environment for individualized English listening and speaking integrating critical thinking. Computers & Education63, 285-305.

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