Educational Data Mining and Learning Analytics: Applications to Constructionist Research

Constructionism can be a powerful framework for teaching complex content to novices. At the core of constructionism is the suggestion that by enabling learners to build creative artifacts that require complex content to function, those learners will have opportunities to learn this content in contextualized, personally-meaningful ways. In this paper, we investigate the relevance of a set of approaches broadly called “educational data mining” or “learning analytics” (henceforth, EDM) to help provide a basis for quantitative research on constructionist learning which does not abandon the richness seen as essential by many researchers in that paradigm. We suggest that EDM may have the potential to support research that is meaningful and useful both to researchers working actively in the constructionist tradition but also to wider communities. Finally, we explore potential collaborations between researchers in the EDM and constructionist traditions; such collaborations have the potential to enhance the ability of constructionist researchers to make rich inference about learning and learners, while providing EDM researchers with many interesting new research questions and challenges. In recent years, project-based, student-centered approaches to education have gained prominence, due in part to an increased demand for higher-level skills in the job market (Levi and Murname, 2004), positive research findings on the effectiveness of such approaches (Barron, Pearson, et al., 2008), and a broader acceptance in public policy circles, as shown, for example, by the Next Generation Science Standards (NGSS Lead States, 2013). While several approaches for this type of learning exist, Constructionism is one of the most popular and well-developed ones (Papert, 1980). In this paper, we investigate the relevance of a set of approaches called “educational data mining” or “learning analytics” (henceforth abbreviated as ‘EDM’) (R. Baker & Yacef, 2009; Romero & Ventura, 2010a; R. Baker & Siemens, in press) to help provide a basis for quantitative research on constructionist learning which does not abandon the richness seen as essential by many researchers in that paradigm. As such, EDM may have the potential to support research that is meaningful and useful both to researchers working actively in the constructionist tradition and to the wider community of learning scientists and policymakers. EDM, broadly, is a set of methods that apply data mining and machine learning techniques such as prediction, classification, and discovery of latent structural regularities to rich, voluminous, and idiosyncratic educational data, potentially similar to those data generated by many constructionist learning environments which allows students to explore and build their own artifacts, computer programs, and media pieces. As such, we identify four axes in which EDM methods may be helpful for constructionist research: 1. EDM methods do not require constructionists to abandon deep qualitative analysis for simplistic summative or confirmatory quantitative analysis; 2. EDM methods can generate different and complementary new analyses to support qualitative research; 3. By enabling precise formative assessments of complex constructs, EDM methods can support an increase in methodological rigor and replicability; 4. EDM can be used to present comprehensible and actionable data to learners and teachers in situ. In order to investigate those axes, we start by describing our perspective on compatibilities and incompatibilities between constructionism and EDM. At the core of constructionism is the suggestion that by enabling learners to build creative artifacts that require complex content to function, those learners will have opportunities to learn that complex content in connected, meaningful ways. Constructionist projects often emphasize making those artifacts (and often data) public, socially relevant, and personally meaningful to learners, and encourage working in social spaces such that learners engage each other to accelerate the learning process. diSessa and Cobb (2004) argue that constructionism serves a framework for action, as it describes its own praxis (i.e., how it matches theory to practice). The learning theory supporting constructionism is classically constructivist, combining concepts from Piaget and Vygotsky (Fosnot, 2005). As constructionism matures as a constructivist framework for action and expands in scale, constructionist projects are becoming both more complex (Reynolds & Caperton, 2011), more scalable (Resnick, Maloney, et al., 2009), and more affordable for schools following significant development in low cost “construction” technologies such as robotics and 3D printers. As such, there have been increasing opportunities to learn more about how students learn in constructionist contexts, advancing the science of learning. These discoveries will have the potential to improve the quality of all constructivist learning experiences. For example, Wilensky and Reisman (2006) have shown how constructionist modeling and simulation can make science learning more accessible, Resnick (1998) has shown how constructionism can reframe programming as art at scale, Buechley & Eisenberg (2008) have used e-textiles to engage female students in robotics, Eisenberg (2011) and Blikstein (2013, 2014) use constructionist digital fabrication to successfully teach programming, engineering, and electronics in a novel, integrated way. The findings of these research and design projects have the potential to be useful to a wide external community of teachers, researchers, practitioners, and other stakeholders. However, connecting findings from the constructionist tradition to the goals of policymakers can be challenging, due to the historical differences in methodology and values between these communities. The resources needed to study such interventions at scale are considerable, given the need to carefully document, code, and analyze each student’s work processes and artifacts. The designs of constructionist research often result in findings that do not map to what researchers, outside interests, and policymakers are expecting, in contrast to conventional controlled studies, which are designed to (more conclusively) answer a limited set of sharply targeted research questions. Due the lack of a common ground to discuss benefits and scalability of constructionist and project-based designs, these designs have been too frequently sidelined to niche institutions such as private schools, museums, or atypical public schools. To understand what the role EDM methods can play in constructionist research, we must frame what we mean by constructionist research more precisely. We follow Papert and Harel (1991) in their situating of constructionism, but they do not constrain the term to one formal definition. The definition is further complicated by the fact that constructionism has many overlaps with other research and design traditions, such as constructivism and socio-constructivism themselves, as well as project-based pedagogies and inquiry-based designs. However, we believe that it is possible to define the subset of constructionism amenable to EDM, a focus we adopt in this article for brevity. In this paper, we focus on the constructionist literature dealing with students learning to construct understandings by constructing (physical or virtual) artifacts, where the students' learning environments are designed and constrained such that building artifacts in/with that environment is designed to help students construct their own understandings. In other words, we are focusing on creative work done in computational environments designed to foster creative and transformational learning, such as NetLogo (Wilensky, 1999), Scratch (Resnick, Maloney, et al., 2009), or LEGO Mindstorms. This sub-category of constructionism can and does generate considerable formative and summative data. It also has the benefit of having a history of success in the classroom. From Papert’s seminal (1972) work through today, constructionist learning has been shown to promote the development of deep understanding of relatively complex content, with many examples ranging from mathematics (Harel, 1990; Wilensky, 1996) to history (Zahn, Krauskopf, Hesse, & Pea, 2010). However, constructionist learning environments, ideas, and findings have yet to reach the majority of classrooms and have had incomplete influence in the broader education research community. There are several potential reasons for this. One of them may be a lack of demonstration that findings are generalizable across populations and across specific content. Another reason is that constructionist activities are seen to be timeconsuming for teachers (Warschauer & Matuchniak, 2010), though, in practice, it has been shown that supporting understanding through project-based work could actually save time (Fosnot, 2005) and enable classroom dynamics that may streamline class preparation (e.g., peer teaching or peer feedback). A last reason is that constructionists almost universally value more deep understanding of scientific principles than facts or procedural skills even in contexts (e.g., many classrooms) in which memorization of facts and procedural skills is the target to be evaluated (Abelson & diSessa, 1986; Papert & Harel, 1991). Therefore, much of what is learned in constructionist environments does not directly translate to test scores or other established metrics. Constructionist research can be useful and convincing to audiences that do not yet take full advantage of the scientific findings of this community, but it requires careful consideration of framing and evidence to reach them. Educational data mining methods pose the potential to both enhance constructionist research, and to support constructionist researchers in communicating their findings in a fashion that other researchers consider valid. Blikstein (2011, p. 110) made