Naturally occurring data as research instrument

Lister, Raymond; Clear, Tony; Simón,; Bouvier, Dennis; Carter, Paul; Eckerdal, Anna; Jacková, Jana; Lopez, Mike; McCartney, Robert; Robbins, Phil; Seppälä, Otto; Thompson, Errol

doi:10.1145/1709424.1709460

Cited by 98 publications

(8 citation statements)

References 25 publications

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“…Further evidence proves relationships between code tracing, code writing, and code explaining (Lister et al 2009; Venables et al 2009). A strong positive correlation was found between code tracing and code writing (Lister et al 2010). Harrington and Cheng (2018) conducted a study with 384 students, to investigate whether a gap exists between the ability of students to trace and write code.…”

Section: Coding Skillsmentioning

confidence: 99%

Investigating the effects of learning activities in a mobile Python tutor for targeting multiple coding skills

Fabic

Mitrović

Neshatian

2018

RPTEL

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Mobile devices are increasingly being utilized for learning due to their unique features including portability for providing ubiquitous experiences. In this paper, we present PyKinetic, a mobile tutor we developed for Python programming, aimed to serve as a supplement to traditional courses. The overarching goal of our work is to design coding activities that maximize learning. As we work towards our goal, we first focus on the learning effectiveness of the activities within PyKinetic, rather than evaluating the effectiveness of PyKinetic as a supplement resource for an introductory programming course. The version of PyKinetic (PyKinetic_DbgOut) used in the study contains five types of learning activities aimed at supporting debugging, code-tracing, and code writing skills. We evaluated PyKinetic in a controlled lab study with quantitative and qualitative results to address the following research questions: (R1) Is the combination of coding activities effective for learning programming? (R2) How do the activities affect the skills of students with lower prior knowledge (novices) compared to those who had higher prior knowledge (advanced)? (R3) How can we improve the usability of PyKinetic? Results revealed that PyKinetic_DbgOut was more beneficial for advanced students. Furthermore, we found how coding skills are interrelated differently for novices compared to advanced learners. Lastly, we acquired sufficient feedback from the participants to improve the tutor.

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Section: Coding Skillsmentioning

confidence: 99%

Investigating the effects of learning activities in a mobile Python tutor for targeting multiple coding skills

Fabic

Mitrović

Neshatian

2018

RPTEL

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“…In this article, we try to improve the novice programming outcomes by using roles of variables and relational reasoning about the code. To read or write a program, students need a holistic point of view as well as a local perspective (Lister et al, 2010). Using variables often requires a relatively local perspective, while understanding and writing a program needs a holistic approach to build multistructure or relation in a program (Sheard et al, 2008).…”

Section: Role Informal Definitionmentioning

confidence: 99%

“…SOLO can be utilized as a general measure of different disciplines because it is content independent (Jimoyiannis, 2013). Existing research have shown that SOLO is a reliable taxonomy to evaluate the students' learning in various subjects, such as computer science (Lister et al, 2010), mathematics (Ç etin & Ilhan, 2017), and physical therapy (Zipp, Maher, Donnelly, Fritz, & Snowdon, 2016), and so forth.…”

Section: Solo Taxonomymentioning

confidence: 99%

“…SOLO taxonomy, based on the amount of the learner knows (quantitative) and the integration of the detail into a structural pattern (qualitative), describes the growing complexity of a learner's activity by identifying the levels of students' performance (Jakosˇ& Verber, 2016). By using this taxonomy, students' answers can be classified according to the level of integration that they demonstrate rather than absolute correctness (Lister et al, 2010). SOLO offers a way of describing the growing complexity and hierarchy of a learner's activity: being able to look at only one tree, seeing a few isolated trees, or seeing a whole forest (J. L. Whalley et al, 2006).…”

Section: Solo Taxonomymentioning

confidence: 99%

“…In this research, the SOLO categories for program comprehension are used for evaluating the reading skills and SOLO categories for construction were used for the writing skills. Lister et al (2010) were used to evaluate the student's ability of reading and understanding in this article. Studies have shown these SOLO categories are reliable for classifying code, that is, the student responses to comprehension questions (Clear et al, 2008;Lister et al, 2006;Sheard et al, 2008).…”

Section: Solo Taxonomymentioning

confidence: 99%

See 2 more Smart Citations

Evaluating the Effectiveness Roles of Variables in the Novice Programmers Learning

Shi

Cui

Ping

et al. 2017

Journal of Educational Computing Research

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This research applies the roles of variables to the novice programmers in the C language programming. The results are evaluated using the Structure of Observed Learning Outcomes (SOLO) taxonomy. The participants were divided into an experimental group and a control group. The students from the control group learned programming in the traditional case-based teaching method. The students in the experimental group learned programming by applying the role-based approach to the case-based teaching method. For the purposes of determining the effects of the role-based teaching, the SOLO taxonomy was used to evaluate the results of the participants' final paper-pencil test. The SOLO level of the code reading and code writing was graded according to the SOLO categories for program comprehension and construction. Data analyses show that (a) there was a higher level of performance and cognitive ability in the experimental group, (b) there were some positive and significant correlations between student's final examination scores and their SOLO level of the code reading and code writing, and (c) the SOLO level of the code writing and code reading was significantly related. These results suggest that the role of variable is effective on improving novice programming.

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Smart generation of code tracing questions for assessment in introductory programming

Stankov

Jovanov

Bogdanova

2022

Comp Applic In Engineering

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Teaching programming is a challenging activity nowadays, especially in introductory programming courses, which are typically massively attended. Writing functional programs is a cognitive skill, which many students, novices in programming, find it difficult to master. It is equally challenging to assess this ability of students. Research has shown that students need to learn how to read and understand, before they can learn to write programs. Code tracing questions are a suitable way to assess the knowledge of semantics of programming constructs (understanding what programs do). However, when designing these types of questions, teachers need to be aware of the complexity of the code and must always try to create questions with code snippets of same or similar complexity, especially when preparing different versions of the same test (for large student groups). In this paper we propose a new model for smart automatic generation of code tracing questions. The model uses a suitable source code metric to measure the code complexity, and to enable generation of questions containing code snippets of consistent complexity. We also introduce CodeCPP: a newly developed Moodle plugin, which is an implementation of the proposed model. The evaluation of CodeCPP, through a year‐long use of the system in teaching programming as well as feedback from selected teachers with many years of experience in teaching programming courses, shows positive and promising results. The system utilization confirms that the application of the proposed model enables a fair and objective knowledge assessment. Furthermore, the feedback provided by teachers and students is highly positive.

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Naturally occurring data as research instrument

Cited by 98 publications

References 25 publications

Investigating the effects of learning activities in a mobile Python tutor for targeting multiple coding skills

Investigating the effects of learning activities in a mobile Python tutor for targeting multiple coding skills

Evaluating the Effectiveness Roles of Variables in the Novice Programmers Learning

Smart generation of code tracing questions for assessment in introductory programming

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