Browsing by Author "Farghally, Mohammed Fawzi Seddik"

Now showing 1 - 4 of 4
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    Analyzing Student Session Data in an eTextbook
    Heo, Samnyeong (Virginia Tech, 2022-07-18)
    As more students interact with online learning platforms and eTextbooks, they generate massive amounts of data. For example, the OpenDSA eTextbook system collects clickstream data as users interact with prose, visualizations, and interactive auto-graded exercises. Ideally, instructors and system developers can harness this information to create better instructional experiences. But in its raw event-level form, it is difficult for developers or instructors to understand student behaviors, or to make testable hypotheses about relationships between behavior and performance. In this study, we describe our efforts to break raw event-level data first into sessions (a continuous series of work by a student) and then to meaningfully abstract the events into higher-level descriptions of that session. The goal of this abstraction is to help instructors and researchers gain insights into the students' learning behaviors. For example, we can distinguish when students read material and then attempt the associated exercise, versus going straight to the exercise and then hunting for the answers in the associated material. We first bundle events into related activities, such as the events associated with stepping through a given visualization, or with working a given exercise. Each such group of events defines a state. A state is a basic unit that characterizes the interaction log data, and there are multiple state types including reading prose, interacting with visual contents, and solving exercises. We harnessed the abstracted data to analyze studying behavior and compared it with course performance based on GPA. We analyzed data from the Fall 2020 and Spring 2021 sections of a senior-level Formal Languages course, and also from the Fall 2020 and Spring 2021 sections of a data structures course.
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    Detecting Credit-Seeking Behavior on Programmed Instruction Framesets
    Elnady, Yusuf Fawzy (Virginia Tech, 2022-06-02)
    When students use an online eTextbook with content and interactive graded exercises, they often display aspects of two types of behavior: credit-seeking, and knowledge-seeking. Any given student might behave to some degree in either way in a given assignment. In this work, we look at multiple aspects of detecting the degree to which either behavior is taking place, and investigate relationships to student performance. In particular, we focus on an eTextbook used for teaching Formal Languages, an advanced computer science course. This eTextbook is using Programmed Instruction (PI) framesets to deliver the material. We take two approaches to analyze session interactions in order to detect credit-seeking incidents. We first start with a coarse-grained approach by presenting an unsupervised model that clusters the behavior in the work sessions based on the sequence of different interactions that happens during them. Then we perform a fine-grained analysis where we consider the type of each question in the frameset, which can be a multi-choice, single-choice, or T/F question. We show that credit-seeking behavior is negatively affecting the learning outcome of the students. We also find that the type of the PI frame is a key factor in drawing students more into the credit-seeking behavior to finish the PI framesets quickly. We implement three machine learning models that predict students' midterm and overall semester grades based on their amount of credit-seeking behavior on the PI framesets. Finally, we provide a semisupervised learning model to aid in the work session labeling process.
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    Understanding the Preparation Phase of Technical Interviews
    Bell, Brian Alexander (Virginia Tech, 2023-07-05)
    Technical coding interviews are a core part of the evaluation process of software engineering (SWE) applicants. This process requires SWE job seekers to typically complete either one or multiple rounds of such interviews which are designed to measure their technical understanding of software engineering topics such as Data Structures and Algorithms. However, there are additional considerations made during the evaluation process, such as the applicants behavioral skills. Skills such as communication, problem solving, and stress tolerance, are just some of the skills that may be used in addition to the general technical skills. Both of these skill areas, in addition to the cognitive and social complexities associated with the various types of interviewing environments, result in a challenging event in which to properly prepare for. Our research aims to better understand how SWE job seekers prepare for such interviews through survey usage which analysis their used services, study habits, and educational background, thus providing useful insight and understanding into the complexities associated with the preparation process as a whole. By understanding the challenges and practices associated with the preparation phase of technical interviews, our research aims to help SWE job seekers, hiring companies, and future research creation.
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    Visualizing Algorithm Analysis Topics
    Farghally, Mohammed Fawzi Seddik (Virginia Tech, 2016-11-30)
    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.