Data science knowledge integration: Affordances of a computational cognitive apprenticeship on student conceptual understanding

Abstract

This study implements a computational cognitive apprenticeship framework for knowledge integration of Data Science (DS) concepts delivered via computational notebooks. This study also explores students' conceptual understanding of the unsupervised Machine Learning algorithm of K‐means after being exposed to this method. The learning of DS methods and techniques has become paramount for the new generations of undergraduate engineering students. However, little is known about effective strategies to support student learning of DS and machine learning (ML) algorithms. The research questions are: How do students conceptualize their understanding of an unsupervised ML method after engaging with interactive visualizations designed using the computational cognitive apprenticeship approach? How do the affordances of the interactive visualizations support or hinder student knowledge integration of an unsupervised machine learning method? Design‐based research allowed for the iterative design, implementation, and validation of the pedagogy in the context of a working classroom. For this, data collection methods often take the form of student artifacts. We performed a qualitative content analysis of students' written responses and reflections elicited during the learning process. Results suggest that the computational cognitive apprenticeship promoted knowledge integration. After interacting with the computational notebooks, most students had accurate conceptions of the goal and the nature of the method and identified factors affecting the output of the algorithm. Students found it useful to have a concrete representation of the method, which supported its conceptual understanding and showcased the acquisition of strategic knowledge for its appropriate execution. However, we also identified important misconceptions students held about the algorithm.

Undergraduate and graduate students' conceptual understanding of model classification outcomes under the lens of scientific argumentation

“…To further discuss how pedagogical elements of the scientific argumentation framework can enhance student performance, it was found that the implementation of a computational cognitive apprenticeship approach significantly impacted the students' understanding of the various types of ML models [13, 41]. As outlined in Section 4, the learning activity that encompassed this research study scaffolded the student through learning foundational knowledge, providing examples of the desired output, and prompting self‐regulated practice.…”

“…While the context of the data collection is discussed further in the methodology below, it is important to draw attention to the fact that students worked through a scaffolded computational environment. The design of the learning activity drew inspiration from all four components of a computational cognitive apprenticeship model: content, method, sequencing, and sociology [8], which has successfully supported conceptual understanding in the context of data science education [41]. We briefly describe how the computational cognitive apprenticeship supported the learning process.…”

“…This research can help uncover the "black box" of learning and allow researchers to develop educational material and pedagogy suited for domain-specific problems [38]. For instance, using computational notebooks (along with peer discussions) can help promote students' domain knowledge [39,41]. Then, alongside this enhanced conceptual understanding of the field, students who employ scientific argumentation techniques can help researchers glean insight into the reasoning processes and knowledge-integration on a topic [5,21,29].…”

“…Then, alongside this enhanced conceptual understanding of the field, students who employ scientific argumentation techniques can help researchers glean insight into the reasoning processes and knowledge-integration on a topic [5,21,29]. To our knowledge, Sánchez-Peña et al [41], who investigated students' conceptual understanding as they learn and work through data science concepts and the K-Means algorithm, may be the only published study to date that is focused on students' conceptualizations in close relation to the ML and AI field.…”