Setting the Scope of Concept Inventories for Introductory Computing Subjects

Goldman, Ken; Gross, Paul; Heeren, Cinda; Herman, Geoffrey L.; Kaczmarczyk, Lisa C.; Loui, Michael C.; Zilles, Craig

doi:10.1145/1789934.1789935

Cited by 102 publications

(47 citation statements)

References 25 publications

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“…Tew and Guzdial [23] used the contents of textbooks and the ACM's Computing Curricula 2001 to identify ten critical topics. Goldman et al [8] engaged instructors in the creation of a concept list for three areas of CS using a structured approach for negotiating consensus among experts. For CS1, the result was a list of 64 topics.…”

Section: The Initial Data Instrumentmentioning

confidence: 99%

“…Our instrument drew concepts from two of these CS1 concept inventories [17,8] and the study of problem distributions [15]. Concepts identified in all three studies were included.…”

Section: The Initial Data Instrumentmentioning

confidence: 99%

“…A number of groups have worked to develop exam questions that more precisely evaluate specific programming topics [3,11,5], and other groups have worked to enumerate the concepts included in CS1 courses [8] or in data structure exams [20]. However, to our knowledge, no one has reviewed CS1 exams from a range of schools and attempted to identify the skills and concepts required by each question.…”

Section: Introductionmentioning

confidence: 99%

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Reviewing CS1 exam question content

Petersen

Craig

Zingaro

2011

Proceedings of the 42nd ACM Technical Symposium on Computer Science Education

106

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Many factors have been cited for poor performance of students in CS1. To investigate how assessment mechanisms may impact student performance, nine experienced CS1 instructors reviewed final examinations from a variety of North American institutions. The majority of the exams reviewed were composed predominantly of high-value, integrative codewriting questions, and the reviewers regularly underestimated the number of CS1 concepts required to answer these questions. An evaluation of the content and cognitive requirements of individual questions suggests that in order to succeed, students must internalize a large amount of CS1 content. This emphasizes the need for focused assessment techniques to provide students with the opportunity to demonstrate their knowledge.

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Section: The Initial Data Instrumentmentioning

confidence: 99%

“…Our instrument drew concepts from two of these CS1 concept inventories [17,8] and the study of problem distributions [15]. Concepts identified in all three studies were included.…”

Section: The Initial Data Instrumentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Reviewing CS1 exam question content

Petersen

Craig

Zingaro

2011

Proceedings of the 42nd ACM Technical Symposium on Computer Science Education

106

View full text Add to dashboard Cite

show abstract

“…In the field of computer science, concept inventories have been created for digital logic [16,17] using the Delphi process, algorithms and data structures [11], and introductory courses [13,14,19]. Although these CIs are a good start, the majority of computer science topics remain without concept inventories.…”

Section: Related Workmentioning

confidence: 99%

Leveraging open source principles for flexible concept inventory development

Porter

Taylor

Webb

2014

Proceedings of the 2014 Conference on Innovation &Amp; Technology in Computer Science Education - ITiCSE '14

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Concept Inventory (CI) assessments, which target high-level learning goals, have proven highly valuable for higher education research. These assessments have helped to evaluate pedagogical practices among individual instructors, both within and across institutions, and have hence elevated the level of discourse on education within the community. The success of CIs in physics has inspired similar developments in computer science, with a few CIs now developed for computer science courses. However, the development of a CI typically follows a burdensome process, requiring a significant investment to produce a single CI that may be difficult to deploy due to institutional curricular differences. Furthermore, as our field continues to be shaped by technological advances, a path to faster, more modular CI development is critical. This paper proposes an alternative CI development model and continues the discussion within the community about the need for, and path to, concept inventories throughout the computer science curriculum. Specifically, we explore the implications of an open collaboration system for CI development that would mimic the principles common to open source software communities, which have regularly demonstrated their ability to produce high-quality results.

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“…The automatically graded questions are implemented in a generic text-based file format, which can be imported into any course management system. The questions are developed based on widely accepted concept inventories for computer engineering courses [31][32][33][34]. Figure 1 shows the environment that facilitates the skill development and formative assessment for these course.…”

Section: Course Materials Designmentioning

confidence: 99%

Deploying Adaptive Learning Environments to Overcome Background Deficiencies and Facilitate Mastery of Computer Engineering Content

LaMeres¹

2015 ASEE Annual Conference and Exposition Proceedings

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Dr. Brock J. LaMeres is an Associate Professor in the electrical and computer engineering department at Montana State University. LaMeres teaches and conducts research in the area of digital systems and engineering education. LaMeres is currently studying the effectiveness of online delivery of engineering content including the impact of adaptive learning modules. LaMeres is also studying how different student demographics use e-learning content and how the material can be modified to provide a personalized learning experience. LaMeres received his Ph.D. from the University of Colorado, Boulder. He has published over 70 manuscripts and 2 textbooks in the area of digital systems and engineering education. LaMeres has also been granted 13 US patents in the area of digital signal propagation. LaMeres is a Senior Member of IEEE, a member of ASEE, and is a registered Professional Engineer in the States of Montana and Colorado. Prior to joining the MSU faculty, LaMeres worked as an R&D engineer for Agilent Technologies in Colorado Springs, CO where he designed electronic test equipment.c American Society for Engineering Education, 2015 Page 26.444.1 Using Adaptive Learning Environments to Overcome Background Deficiencies and Facilitate Mastery of Computer Engineering Content AbstractThis paper describes the use of web-based adaptive learning modules to improve student mastery of computer engineering concepts. Adaptive learning is an exciting pedagogical approach that can provide individual instruction to students by dynamically altering the difficulty of content based on an ongoing assessment of the students' capability. This technique has recently become practical for large groups of students due to advances in course management systems. The computer-based adaptive material can guide a student through a set of incrementally difficult exercises. This has been shown to be nearly as effective as a live instructor guiding the student through the material. What makes computer-administered adaptive learning appealing is that it can reach an expansive student body. This provides a broader reach than can be achieved by instructor-administered guidance, which is limited by the instructor's availability. Another potential impact of adaptive learning is tailoring the material to the demographics of the students. This can be as simple as the wording used in the problem or posing the problem as a relevant example of interest to a particular student group. This approach has the potential to enhance student perception of the value of the content. This leads to increased content retention and improves student motivation to excel in the course. This paper will discuss the work being conducted at Montana State University in developing and deploying adaptive learning modules at a diverse set of universities to collect data on how different student groups use and are impacted by the materials. Background 1.

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Setting the Scope of Concept Inventories for Introductory Computing Subjects

Cited by 102 publications

References 25 publications

Reviewing CS1 exam question content

Reviewing CS1 exam question content

Leveraging open source principles for flexible concept inventory development

Deploying Adaptive Learning Environments to Overcome Background Deficiencies and Facilitate Mastery of Computer Engineering Content

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