ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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- Creators: Hsiao, I-Han
Description
Online learning platforms such as massive online open courses (MOOCs) and
intelligent tutoring systems (ITSs) have made learning more accessible and personalized. These systems generate unprecedented amounts of behavioral data and open the way for predicting students’ future performance based on their behavior, and for assessing their strengths and weaknesses in learning.
This thesis attempts to mine students’ working patterns using a programming problem solving system, and build predictive models to estimate students’ learning. QuizIT, a programming solving system, was used to collect students’ problem-solving activities from a lower-division computer science programming course in 2016 Fall semester. Differential mining techniques were used to extract frequent patterns based on each activity provided details about question’s correctness, complexity, topic, and time to represent students’ behavior. These patterns were further used to build classifiers to predict students’ performances.
Seven main learning behaviors were discovered based on these patterns, which provided insight into students’ metacognitive skills and thought processes. Besides predicting students’ performance group, the classification models also helped in finding important behaviors which were crucial in determining a student’s positive or negative performance throughout the semester.
intelligent tutoring systems (ITSs) have made learning more accessible and personalized. These systems generate unprecedented amounts of behavioral data and open the way for predicting students’ future performance based on their behavior, and for assessing their strengths and weaknesses in learning.
This thesis attempts to mine students’ working patterns using a programming problem solving system, and build predictive models to estimate students’ learning. QuizIT, a programming solving system, was used to collect students’ problem-solving activities from a lower-division computer science programming course in 2016 Fall semester. Differential mining techniques were used to extract frequent patterns based on each activity provided details about question’s correctness, complexity, topic, and time to represent students’ behavior. These patterns were further used to build classifiers to predict students’ performances.
Seven main learning behaviors were discovered based on these patterns, which provided insight into students’ metacognitive skills and thought processes. Besides predicting students’ performance group, the classification models also helped in finding important behaviors which were crucial in determining a student’s positive or negative performance throughout the semester.
ContributorsMandal, Partho Pratim (Author) / Hsiao, I-Han (Thesis advisor) / Davulcu, Hasan (Committee member) / Tong, Hanghang (Committee member) / Arizona State University (Publisher)
Created2017
Description
Learning programming involves a variety of complex cognitive activities, from abstract knowledge construction to structural operations, which include program design,modifying, debugging, and documenting tasks. In this work, the objective was to explore and investigate the barriers and obstacles that programming novice learners encountered and how the learners overcome them. Several lab and classroom studies were designed and conducted, the results showed that novice students had different behavior patterns compared to experienced learners, which indicates obstacles encountered. The studies also proved that proper assistance could help novices find helpful materials to read. However, novices still suffered from the lack of background knowledge and the limited cognitive load while learning, which resulted in challenges in understanding programming related materials, especially code examples. Therefore, I further proposed to use the natural language generator (NLG) to generate code explanations for educational purposes. The natural language generator is designed based on Long Short Term Memory (LSTM), a deep-learning translation model. To establish the model, a data set was collected from Amazon Mechanical Turks (AMT) recording explanations from human experts for programming code lines.
To evaluate the model, a pilot study was conducted and proved that the readability of the machine generated (MG) explanation was compatible with human explanations, while its accuracy is still not ideal, especially for complicated code lines. Furthermore, a code-example based learning platform was developed to utilize the explanation generating model in programming teaching. To examine the effect of code example explanations on different learners, two lab-class experiments were conducted separately ii in a programming novices’ class and an advanced students’ class. The experiment result indicated that when learning programming concepts, the MG code explanations significantly improved the learning Predictability for novices compared to control group, and the explanations also extended the novices’ learning time by generating more material to read, which potentially lead to a better learning gain. Besides, a completed correlation model was constructed according to the experiment result to illustrate the connections between different factors and the learning effect.
To evaluate the model, a pilot study was conducted and proved that the readability of the machine generated (MG) explanation was compatible with human explanations, while its accuracy is still not ideal, especially for complicated code lines. Furthermore, a code-example based learning platform was developed to utilize the explanation generating model in programming teaching. To examine the effect of code example explanations on different learners, two lab-class experiments were conducted separately ii in a programming novices’ class and an advanced students’ class. The experiment result indicated that when learning programming concepts, the MG code explanations significantly improved the learning Predictability for novices compared to control group, and the explanations also extended the novices’ learning time by generating more material to read, which potentially lead to a better learning gain. Besides, a completed correlation model was constructed according to the experiment result to illustrate the connections between different factors and the learning effect.
ContributorsLu, Yihan (Author) / Hsiao, I-Han (Thesis advisor) / VanLehn, Kurt (Committee member) / Tong, Hanghang (Committee member) / Yang, Yezhou (Committee member) / Price, Thomas (Committee member) / Arizona State University (Publisher)
Created2020