Using Learning Analytics to Understand the Learning Pathways of Novice Programmers

Berland, Matthew; Martin, Taylor; Benton, Tom; Smith, Carmen Petrick; Davis, Don

doi:10.1080/10508406.2013.836655

Cited by 177 publications

(91 citation statements)

References 45 publications

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“…Specifically, they were able to observe that hardly any students used a particular tool (the debugger) during the course. Berland, Martin, Benton, Petrick Smith, and Davis (2013) discuss the importance of tinkering in the learning process. To measure it, they capture the various states of a program as a student edits it.…”

Section: Introductionmentioning

confidence: 98%

Utilizing student activity patterns to predict performance

Casey

Azcona

2017

Int J Educ Technol High Educ

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Apart from being able to support the bulk of student activity in suitable disciplines such as computer programming, Web-based educational systems have the potential to yield valuable insights into student behavior. Through the use of educational analytics, we can dispense with preconceptions of how students consume and reuse course material. In this paper, we examine the speed at which students employ concepts which they are being taught during a semester. To show the wider utility of this data, we present a basic classification system for early detection of poor performers and show how it can be improved by including data on when students use a concept for the first time. Using our improved classifier, we can achieve an accuracy of 85% in predicting poor performers prior to the completion of the course.

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Section: Introductionmentioning

confidence: 98%

Utilizing student activity patterns to predict performance

Casey

Azcona

2017

Int J Educ Technol High Educ

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“…Construction and fabrication brings this cross-domain nature of powerful ideas to the forefront. Embedding computation in fashion with a Lilypad Arduino (Buechley et al 2008;Kafai et al 2010); controlling a video game character with bananas, clay, and a Makey Makey Millner 2010); programming a soccer-playing robot to score goals (Berland 2016;Berland et al 2013); or making a mechanical wooden roller coaster (Blikstein 2013) each extend across traditional class subjects and engage learners with powerful ideas such as electricity flow, algorithmic thinking, and the conservation of energy. Furthermore, such activities connect hobbies explored informally with topics typically reserved for the classroom and can reach across gender and cultural boundaries inherent in some materials, contexts, and domains (Buechley and Perner-Wilson 2012;Kafai and Burke 2014;Holbert 2016).…”

Section: Powerful Ideasmentioning

confidence: 99%

The powerful ideas of making: building beyond the curriculum

Holbert

2016

J Innov Entrep

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In this paper, rather than consider how fabrication technologies and maker activities can be directly matched to existing standards or allow for more efficient content coverage, I position making as a network of powerful ideas and perspectives that are highly connected to other ideas, personal and syntonic to the learner, and are immediately useful for solving problems of personal importance. Furthermore, I argue that creating learning environments where learners encounter and take ownership of these powerful ideas requires instructors, coaches, administrators, and designers that are committed to creating space for the learner to make meaningful choices, to tinker and explore, and to collaborate. Bots for Tots, a project that engages children in making toys for members of their community, is briefly described to highlight the affordances and challenges of implementing design features that focus on engaging learners with powerful ideas.

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“…Siemens & Baker, 2015). These methods were incorporated into the line of research discussed here, and using these techniques, some new and exciting metrics have been added to code-analyzing, including more complex structure-based features, as well as variables measuring student-computer interaction (e.g., Berland, Martin, Benton, Smith, & Davis, 2013;Blikstein, 2011;Taherkhani & Malmi, 2013;Vihavainen, Luukkainen, & Kurhila, 2013). In this paper, we take an EDM approach, in particular applying clustering analysis and building a prediction model, based on a comprehensive set of software metrics for both quality and security.…”

Section: Software Metricsmentioning

confidence: 99%

Software Quality and Security in Teachers' and Students' Codes When Learning a New Programming Language

Boutnaru¹,

Hershkovitz²

2015

IJELL

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In recent years, schools (as well as universities) have added cyber security to their computer science curricula. This topic is still new for most of the current teachers, who would normally have a standard computer science background. Therefore the teachers are trained and then teaching their students what they have just learned. In order to explore differences in both populations’ learning, we compared measures of software quality and security between high-school teachers and students. We collected 109 source files, written in Python by 18 teachers and 31 students, and engineered 32 features, based on common standards for software quality (PEP 8) and security (derived from CERT Secure Coding Standards). We use a multi-view, data-driven approach, by (a) using hierarchical clustering to bottom-up partition the population into groups based on their code-related features and (b) building a decision tree model that predicts whether a student or a teacher wrote a given code (resulting with a LOOCV kappa of 0.751). Overall, our findings suggest that the teachers’ codes have a better quality than the students’ – with a sub-group of the teachers, mostly males, demonstrate better coding than their peers and the students – and that the students’ codes are slightly better secured than the teachers’ codes (although both populations show very low security levels). The findings imply that teachers might benefit from their prior knowledge and experience, but also emphasize the lack of continuous involvement of some of the teachers with code-writing. Therefore, findings shed light on computer science teachers as lifelong learners. Findings also highlight the difference between quality and security in today’s programming paradigms. Implications for these findings are discussed.

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Using Learning Analytics to Understand the Learning Pathways of Novice Programmers

Cited by 177 publications

References 45 publications

Utilizing student activity patterns to predict performance

Utilizing student activity patterns to predict performance

The powerful ideas of making: building beyond the curriculum

Software Quality and Security in Teachers' and Students' Codes When Learning a New Programming Language

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