A Special Issue of Journal of the Learning Sciences
Guest Editors: Michelle Wilkerson and Joseph L. Polman
The emerging field of Data Science has had a large impact on science and society. This has led to over a decade of calls to establish a corresponding field of Data Science Education (Berman et al., 2016; Cleveland, 2001; Finzer, 2013). Initial efforts to do so, while productive, have focused primarily on curricular structures and materials rather than learner knowledge and experience. There is still a need to more deeply conceptualize what makes learning Data Science sufficiently different that it requires a new field of study, and to explore the theoretical and practical implications of these differences for constructing an ethical and effective Data Science Education.
This special issue of Journal of the Learning Sciences (JLS) will explore one key feature of Data Science that we argue has serious implications for education and research: learners’ relationships to data. Data Science is typically concerned with data that is at once personal and opaque—collected in incidental, automated, or unknown ways from activities and contexts within which learners themselves are deeply situated. Emerging research suggests this can significantly impact how learners engage with and make sense of these data—limiting opportunities to learn when these relationships are not recognized by educators or designers (Philip, Olivares-Pasillas, & Rocha, 2016; Rubel, Lim, Hall-Wieckert, & Sullivan, 2016) and enriching them when they are leveraged (Kahn, 2017; Lee, 2013; Taylor & Hall, 2013). The goal of “Situating Data Science” is to sketch the contours of what a Data Science Education might entail given these relationships. More specifically, we ask: In what ways do learners’ relationships relative to data, the contexts from which data are derived, and the tools and practices of data science, shape how they engage with and make sense of data? And, How might learners’ prior experiences with and relationships to data equip them for formal and structured Data Science Education experiences?
We invite contributions that explore how learners’ situatedness—relative to data, and relative to the field of data science—can impact learning in ways that necessitate new lines of research, new theoretical and methodological development, and new approaches to educational design and practice. We use the term situated (Brown, Collins, & Duguid, 1989; Lave & Wenger, 1991) in its broadest sense, to refer to a collection of approaches toward learning, cognition, and participation that we expect can grant new insight into the complex territory marked by the emerging fields of Data Science and Data Science Education. Papers may focus on questions such as:
• How do learners’ different experiences of the same data context affect their engagement with data, and how might this diversity be leveraged pedagogically?
• How do different framings of data science activity goals (e.g., using data for commercial exploitation, predictive modeling, advocacy, self-monitoring, scientific inquiry) influence learners’ engagement with and treatment of data?
• How are current data scientists, such as practitioners of learning analytics or educational data mining, apprenticed into the discipline? In what ways do such apprenticeships leverage (or not) learners’ own data or learning experiences?
• What might be fruitful theoretical and methodological approaches for uncovering orientations toward or experiences with data that are likely to be especially powerful for supporting formal Data Science Education?
• How can insights about learners’ experiences with the types of datasets, tools, and methods characteristic of Data Science in informal (e.g., home, online, museum, hobbyist, advocacy) contexts inform the design of formal Data Science Education experiences?
Submission Instructions: We are currently soliciting abstracts for proposed papers for the special issue. Abstracts should be no longer than 500 words and be accompanied by up to six keywords. Abstracts should be submitted to firstname.lastname@example.org. Our anticipated timeline is as follows:
Abstracts Due (to email@example.com
Invitations to Submit Drafts Sent : Feb 26, 2018
First Drafts Due (to JLS ScholarOne online submission system) : June 22, 2018
Reviews of First Drafts : September 15, 2018
Ongoing Revisions : October 2018-April 2019
Final Manuscript Submitted : May 31, 2019
Special Issue Publication : Fall 2019
Per JLS editorial policy, all articles which are part of this special issue must be accepted through the journal’s standard review process. We anticipate including six (6) articles in this special issue, as well as an introduction by the guest editors and two commentaries.