dc.contributor.author	Farghally, Mohammed Fawzi Seddik	en
dc.contributor.committeechair	Shaffer, Clifford A.	en
dc.contributor.committeemember	Rodger, Susan H.	en
dc.contributor.committeemember	North, Christopher L.	en
dc.contributor.committeemember	McCrickard, D. Scott	en
dc.contributor.committeemember	Ernst, Jeremy V.	en
dc.contributor.department	Computer Science	en
dc.date.accessioned	2016-12-01T11:17:51Z	en
dc.date.available	2016-12-01T11:17:51Z	en
dc.date.issued	2016-11-30	en
dc.description.abstract	Data Structures and Algorithms (DSA) courses are critical for any computer science curriculum. DSA courses emphasize concepts related to procedural dynamics and Algorithm Analysis (AA). These concepts are hard for students to grasp when conveyed using traditional textbook material relying on text and static images. Algorithm Visualizations (AVs) emerged as a technique for conveying DSA concepts using interactive visual representations. Historically, AVs have dealt with portraying algorithm dynamics, and the AV developer community has decades of successful experience with this. But there exist few visualizations to present algorithm analysis concepts. This content is typically still conveyed using text and static images. We have devised an approach that we term Algorithm Analysis Visualizations (AAVs), capable of conveying AA concepts visually. In AAVs, analysis is presented as a series of slides where each statement of the explanation is connected to visuals that support the sentence. We developed a pool of AAVs targeting the basic concepts of AA. We also developed AAVs for basic sorting algorithms, providing a concrete depiction about how the running time analysis of these algorithms can be calculated. To evaluate AAVs, we conducted a quasi-experiment across two offerings of CS3114 at Virginia Tech. By analyzing OpenDSA student interaction logs, we found that intervention group students spent significantly more time viewing the material as compared to control group students who used traditional textual content. Intervention group students gave positive feedback regarding the usefulness of AAVs to help them understand the AA concepts presented in the course. In addition, intervention group students demonstrated better performance than control group students on the AA part of the final exam. The final exam taken by both the control and intervention groups was based on a pilot version of the Algorithm Analysis Concept Inventory (AACI) that was developed to target fundamental AA concepts and probe students' misconceptions about these concepts. The pilot AACI was developed using a Delphi process involving a group of DSA instructors, and was shown to be a valid and reliable instrument to gauge students' understanding of the basic AA topics.	en
dc.description.abstractgeneral	Data Structures and Algorithms (DSA) courses are critical for any computer science curriculum. DSA courses emphasize concepts related to how an algorithm works and the time and space needed by the algorithm, also known as Algorithm Analysis (AA). These concepts are hard for students to grasp when conveyed using traditional textbook material relying on text and static images. Algorithm Visualizations (AVs) emerged as a technique for conveying DSA concepts using interactive visual representations. Historically, AVs have dealt with portraying how an algorithm works, and the AV developer community has decades of successful experience with this. But there exist few visualizations to present concepts related to algorithm efficiency. This content is typically still conveyed using text and static images. We have devised an approach that we term Algorithm Analysis Visualizations (AAVs), capable of conveying efficiency analysis concepts visually. In AAVs, analysis is presented as a series of slides where each statement of the explanation is connected to visuals that support the sentence. AAVs were tested through a study across two offerings of CS3114 at Virginia Tech. We found that students using AAVs spent significantly more time viewing the material as compared to students who used traditional textual content. Students gave positive feedback regarding the usefulness of AAVs to help them understand the efficiency concepts presented in the course. In addition, students using AAVs demonstrated better performance than students using text on the efficiency part of the final exam. The final exam was based on a pilot version of the Algorithm Analysis Concept Inventory (AACI) that was developed to target fundamental efficiency concepts and probe students’ misconceptions about these concepts. The pilot AACI was developed through a decision making technique involving a group of DSA instructors, and was shown to be a valid and reliable instrument to gauge students’ understanding of the basic efficiency topics.	en
dc.description.degree	Ph. D.	en
dc.format.medium	ETD	en
dc.identifier.other	vt_gsexam:9019	en
dc.identifier.uri	http://hdl.handle.net/10919/73539	en
dc.publisher	Virginia Tech	en
dc.rights	In Copyright	en
dc.rights.uri	http://rightsstatements.org/vocab/InC/1.0/	en
dc.subject	Algorithm Analysis Visualizations	en
dc.subject	Visual Proofs	en
dc.subject	Student Engagement	en
dc.subject	Logged Data Analysis	en
dc.subject	Educational Data Mining	en
dc.subject	Online Learning Environments	en
dc.subject	Performance Evaluation	en
dc.subject	Concept Inventory	en
dc.title	Visualizing Algorithm Analysis Topics	en
dc.type	Dissertation	en
thesis.degree.discipline	Computer Science and Applications	en
thesis.degree.grantor	Virginia Polytechnic Institute and State University	en
thesis.degree.level	doctoral	en
thesis.degree.name	Ph. D.	en

Visualizing Algorithm Analysis Topics

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