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dc.contributor.authorFarghally, Mohammed Fawzi Seddiken
dc.date.accessioned2016-12-01T11:17:51Zen
dc.date.available2016-12-01T11:17:51Zen
dc.date.issued2016-11-30en
dc.identifier.othervt_gsexam:9019en
dc.identifier.urihttp://hdl.handle.net/10919/73539en
dc.description.abstractData 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.format.mediumETDen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectAlgorithm Analysis Visualizationsen
dc.subjectVisual Proofsen
dc.subjectStudent Engagementen
dc.subjectLogged Data Analysisen
dc.subjectEducational Data Miningen
dc.subjectOnline Learning Environmentsen
dc.subjectPerformance Evaluationen
dc.subjectConcept Inventoryen
dc.titleVisualizing Algorithm Analysis Topicsen
dc.typeDissertationen
dc.contributor.departmentComputer Scienceen
dc.description.degreePh. D.en
thesis.degree.namePh. D.en
thesis.degree.leveldoctoralen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.disciplineComputer Science and Applicationsen
dc.contributor.committeechairShaffer, Clifford A.en
dc.contributor.committeememberRodger, Susan H.en
dc.contributor.committeememberNorth, Christopher L.en
dc.contributor.committeememberMcCrickard, D. Scotten
dc.contributor.committeememberErnst, Jeremy V.en


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