Carmen Petrick Smith

AMOEBA: Designing for Collaboration in Computer Science Classrooms through Live Learning Analytics.

AMOEBA is a unique tool to support teachers’ orchestration of collaboration among novice programmers in a non-traditional programming environment. The AMOEBA tool was designed and utilized to facilitate collaboration in a classroom setting in real time among novice middle school and high school programmers utilizing the IPRO programming environment. AMOEBA’s key affordance is supporting teachers’ pairing decisions with real time analyses of students’ programming progressions. Teachers can track which students are working in similar ways; this is supported by real-time graphical log analyses of student activities within the programming environment. Pairing students with support from AMOEBA led to improvements in students’ program complexity and depth. Analyses of the data suggest that the data mining techniques utilized in and the metrics provided by AMOEBA can support instructors in orchestrating cooperation. The primary contributions of this paper are a set of design principles around and a working tool for fostering collaboration in computer science classes.

Learning Fractions by Splitting: Using Learning Analytics to Illuminate the Development of Mathematical Understanding

The struggle with fraction learning in kindergarten through Grade 12 in the United States is a persistent problem and one of the major stumbling blocks to succeeding in higher mathematics. Research into this problem has identified several areas where students commonly struggle with fractions. While there are many theories of fraction learning, none of the research on these theories employs samples large enough to test theories at scale or nuanced enough to demonstrate how learning unfolds over time during instructional activities based on these theories. The work reported here uses learning analytics methods with fine-grained log data from an online fraction game to unpack how splitting (i.e. partitioning a whole into equal-sized parts) impacts learning. Study 1 demonstrated that playing the game significantly improved students’ fraction understanding. In addition, a cluster analysis suggested that exploring splitting was beneficial. Study 2 replicated the learning results, and a cluster analysis showed that compared to early game play, later game play showed more optimal splitting strategies. In addition, in looking at the types of transitions that were possible between a student’s early cluster categorization and later cluster categorization, we found that some types of transitions were more beneficial for learning than others.

Nanogenetic learning analytics: illuminating student learning pathways in an online fraction game

A working understanding of fractions is critical to student success in high school and college math. Therefore, an understanding of the learning pathways that lead students to this working understanding is important for educators to provide optimal learning environments for their students. We propose the use of microgenetic analysis techniques including data mining and visualizations to inform our understanding of the process by which students learn fractions in an online game environment. These techniques help identify important variables and classification algorithms to group students by their learning trajectories.

An Instrument to Measure Teacher Practices to Support Personalized Learning in the Middle Grades

Abstract Reforms to support and expand personalized learning increasingly are being introduced in middle schools across the United States. Personalization, as enacted in response to these reforms, encourages teachers to implement many practices that long have been recommended by advocates of middle grades philosophy. To better understand the practices of middle grades teachers working in schools attempting to implement personalized learning, this article presents a survey instrument to measure teacher practices for personalization in the middle grades. The article describes the formulation and initial administrations of the survey to 232 teachers in 2016 and 165 teachers in 2017. Exploratory factor analysis provided evidence for the presence of factors describing practices for personalized assessment, out-of-school learning, whole group learning in a personalized setting, and technology implementation. Confirmatory factor analysis with the follow-up sample provided additional support for this structure. Data from these two separate survey administrations demonstrated high internal consistency and moderate correlation across the groups of practices. Suggestions for future research using the tool are offered. The survey instrument is included as an appendix.

A fraction activity to understand sorting algorithms

The need for students to understand computational thinking and the fundamentals of computer science (CS) is at an all-time high. Jobs requiring some programming are growing significantly, and CS is the only STEM field that has more jobs available than students studying the field. Currently, CS is not required in American schools; therefore, only a subset of students have access to CS coursework. To address this issue, the Computer Science Teachers Association developed standards detailing the ideas needed to provide a complete CS curriculum for all students. Here, King and Smith present an activity introducing children to the idea of sorting algorithms, while simultaneously providing them with the opportunity to compare and order fractions.