Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
Displaying 1 - 10 of 39
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- All Subjects: Educational technology
- Genre: Masters Thesis
- Genre: Art--Study and teaching (Elementary)
Description
Lots of previous studies have analyzed human tutoring at great depths and have shown expert human tutors to produce effect sizes, which is twice of that produced by an intelligent tutoring system (ITS). However, there has been no consensus on which factor makes them so effective. It is important to know this, so that same phenomena can be replicated in an ITS in order to achieve the same level of proficiency as expert human tutors. Also, to the best of my knowledge no one has looked at student reactions when they are working with a computer based tutor. The answers to both these questions are needed in order to build a highly effective computer-based tutor. My research focuses on the second question. In the first phase of my thesis, I analyzed the behavior of students when they were working with a step-based tutor Andes, using verbal-protocol analysis. The accomplishment of doing this was that I got to know of some ways in which students use a step-based tutor which can pave way for the creation of more effective computer-based tutors. I found from the first phase of the research that students often keep trying to fix errors by guessing repeatedly instead of asking for help by clicking the hint button. This phenomenon is known as hint refusal. Surprisingly, a large portion of the student's foundering was due to hint refusal. The hypothesis tested in the second phase of the research is that hint refusal can be significantly reduced and learning can be significantly increased if Andes uses more unsolicited hints and meta hints. An unsolicited hint is a hint that is given without the student asking for one. A meta-hint is like an unsolicited hint in that it is given without the student asking for it, but it just prompts the student to click on the hint button. Two versions of Andes were compared: the original version and a new version that gave more unsolicited and meta-hints. During a two-hour experiment, there were large, statistically reliable differences in several performance measures suggesting that the new policy was more effective.
ContributorsRanganathan, Rajagopalan (Author) / VanLehn, Kurt (Thesis advisor) / Atkinson, Robert (Committee member) / Burleson, Winslow (Committee member) / Arizona State University (Publisher)
Created2011
Description
ABSTRACT Art educators use a variety of teaching and demonstration methods to convey information to students. With the emergence of digital technology, the standard methods of demonstration are changing. Art demonstrations are now being recorded and shared via the internet through video sharing websites such as YouTube. Little research has been conducted on the effectiveness of video demonstration versus the standard teacher-centered demonstration. This study focused on two different demonstration methods for the same clay sculpture project, with two separate groups of students. The control group received regular teacher-centered demonstration for instruction. The experimental group received a series of YouTube videos for demonstration. Quantitative data include scores of clay sculptures using a four-point scale in three separate categories based on construction abilities. Qualitative data include responses to pre and post-questionnaires along with classroom observations. The data is analyzed to look at the difference, if any, between YouTube instruction and regular teacher-centered instruction on middle school students' ceramic construction abilities. Findings suggest that while the YouTube video method of demonstration appeared to have a slightly greater effect on student construction abilities. Although, both instruction methods proved to be beneficial.
ContributorsLee, Allison (Author) / Erickson, Mary (Thesis advisor) / Young, Bernard (Committee member) / Stokrocki, Mary (Committee member) / Arizona State University (Publisher)
Created2013
Description
Believe It! is an animated interactive computer program that delivers cognitive restructuring to adolescent females' irrational career beliefs. It challenges the irrational belief and offers more reasonable alternatives. The current study investigated the potentially differential effects of Asian versus Caucasian animated agents in delivering the treatment to young Chinese American women. The results suggested that the Asian animated agent was not significantly superior to the Caucasian animated agent. Nor was there a significant interaction between level of acculturation and the effects of the animated agents. Ways to modify the Believe It! program for Chinese American users were recommended.
ContributorsZhang, Xue (Author) / Horan, John J (Thesis advisor) / Homer, Judith (Committee member) / Atkinson, Robert (Committee member) / Arizona State University (Publisher)
Created2013
Description
This study explores the influence of framing and activity type on expectations of learning and enjoyment as well as performance in a paraphrase identification task. In the first experiment, 80 students played one of three activities framed as either a "play" or "learning" task. Students then completed one of three activities; learning only, an educational game, or a play only activity. Results showed that the play frame had an effect on learning expectations prior to completing the activity, but had no effect after completing the activity. Students who completed the educational game scored significantly higher on the posttest learning assessment than those in the play only activity. Pairwise comparisons also indicated that students who completed the educational game performed just as well as the learning only activity when given the posttest learning assessment. Performance in the paraphrase identification task was collected using data logged from student interactions, and it was established that although there was an interaction between performance and activity type, this interaction was due to a significant difference during the second round. These results suggest that framing can influence initial expectations, and educational games can teach a simple writing strategy without distracting from the educational task. A second experiment using 80 students was conducted to determine if a stronger frame would influence expectations and to replicate the effect of activity type on learning and enjoyment. The second study showed no effect of framing on expected or reported enjoyment and learning. The performance results showed a significant interaction between performance and activity type, with the interaction being driven by the first round that students completed. However, the effect of activity type was replicated, suggesting that game features can enhance student enjoyment and are not a detriment to learning simple strategy-based tasks.
ContributorsBrandon, Russell (Author) / McNamara, Danielle S. (Thesis advisor) / Jackson, George T. (Committee member) / Johnson-Glenberg, Mina C. (Committee member) / Arizona State University (Publisher)
Created2013
Description
The discussion board is a facet of online education that continues to confound students, educators, and researchers alike. Currently, the majority of research insists that instructors should structure and control online discussions as well as evaluate such discussions. However, the existing literature has yet to compare the various strategies that instructors have identified and employed to facilitate discussion board participation. How should instructors communicate their expectations online? Should instructors create detailed instructions that outline and model exactly how students should participate, or should generalized instructions be communicated? An experiment was conducted in an online course for undergraduate students at Arizona State University. Three variations of instructional conditions were developed for use in the experiment: (1) detailed, (2) general, and (3) limited. The results of the experiment indentified a pedagogically valuable finding that should positively influence the design of future online courses that utilize discussion boards.
ContributorsButler, Nicholas Dale (Author) / Waldron, Vincent (Thesis advisor) / Kassing, Jeffrey (Committee member) / Wise, John (Committee member) / Arizona State University (Publisher)
Created2012
Description
The field of education has been immensely benefited by major breakthroughs in technology. The arrival of computers and the internet made student-teacher interaction from different parts of the world viable, increasing the reach of the educator to hitherto remote corners of the world. The arrival of mobile phones in the recent past has the potential to provide the next paradigm shift in the way education is conducted. It combines the universal reach and powerful visualization capabilities of the computer with intimacy and portability. Engineering education is a field which can exploit the benefits of mobile devices to enhance learning and spread essential technical know-how to different parts of the world. In this thesis, I present AJDSP, an Android application evolved from JDSP, providing an intuitive and a easy to use environment for signal processing education. AJDSP is a graphical programming laboratory for digital signal processing developed for the Android platform. It is designed to provide utility; both as a supplement to traditional classroom learning and as a tool for self-learning. The architecture of AJDSP is based on the Model-View-Controller paradigm optimized for the Android platform. The extensive set of function modules cover a wide range of basic signal processing areas such as convolution, fast Fourier transform, z transform and filter design. The simple and intuitive user interface inspired from iJDSP is designed to facilitate ease of navigation and to provide the user with an intimate learning environment. Rich visualizations necessary to understand mathematically intensive signal processing algorithms have been incorporated into the software. Interactive demonstrations boosting student understanding of concepts like convolution and the relation between different signal domains have also been developed. A set of detailed assessments to evaluate the application has been conducted for graduate and senior-level undergraduate students.
ContributorsRanganath, Suhas (Author) / Spanias, Andreas (Thesis advisor) / Tepedelenlioğlu, Cihan (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2013
Description
Calculus as a math course is important subject students need to succeed in, in order to venture into STEM majors. This thesis focuses on the early detection of at-risk students in a calculus course which can provide the proper intervention that might help them succeed in the course. Calculus has high failure rates which corroborates with the data collected from Arizona State University that shows that 40% of the 3266 students whose data were used failed in their calculus course.This thesis proposes to utilize educational big data to detect students at high risk of failure and their eventual early detection and subsequent intervention can be useful. Some existing studies similar to this thesis make use of open-scale data that are lower in data count and perform predictions on low-impact Massive Open Online Courses(MOOC) based courses. In this thesis, an automatic detection method of academically at-risk students by using learning management systems(LMS) activity data along with the student information system(SIS) data from Arizona State University(ASU) for the course calculus for engineers I (MAT 265) is developed. The method will detect students at risk by employing machine learning to identify key features that contribute to the success of a student.
This thesis also proposes a new technique to convert this button click data into a button click sequence which can be used as inputs to classifiers. In addition, the advancements in Natural Language Processing field can be used by adopting methods such as part-of-speech (POS) tagging and tools such as Facebook Fasttext word embeddings to convert these button click sequences into numeric vectors before feeding them into the classifiers. The thesis proposes two preprocessing techniques and evaluates them on 3 different machine learning ensembles to determine their performance across the two modalities of the class.
ContributorsDileep, Akshay Kumar (Author) / Bansal, Ajay (Thesis advisor) / Cunningham, James (Committee member) / Acuna, Ruben (Committee member) / Arizona State University (Publisher)
Created2021
Description
One persisting problem in Massive Open Online Courses (MOOCs) is the issue of student dropout from these courses. The prediction of student dropout from MOOC courses can identify the factors responsible for such an event and it can further initiate intervention before such an event to increase student success in MOOC. There are different approaches and various features available for the prediction of student’s dropout in MOOC courses.In this research, the data derived from the self-paced math course ‘College Algebra and Problem Solving’ offered on the MOOC platform Open edX offered by Arizona State University (ASU) from 2016 to 2020 was considered. This research aims to predict the dropout of students from a MOOC course given a set of features engineered from the learning of students in a day. Machine Learning (ML) model used is Random Forest (RF) and this model is evaluated using the validation metrics like accuracy, precision, recall, F1-score, Area Under the Curve (AUC), Receiver Operating Characteristic (ROC) curve. The average rate of student learning progress was found to have more impact than other features. The model developed can predict the dropout or continuation of students on any given day in the MOOC course with an accuracy of 87.5%, AUC of 94.5%, precision of 88%, recall of 87.5%, and F1-score of 87.5% respectively. The contributing features and interactions were explained using Shapely values for the prediction of the model. The features engineered in this research are predictive of student dropout and could be used for similar courses to predict student dropout from the course. This model can also help in making interventions at a critical time to help students succeed in this MOOC course.
ContributorsDominic Ravichandran, Sheran Dass (Author) / Gary, Kevin (Thesis advisor) / Bansal, Ajay (Committee member) / Cunningham, James (Committee member) / Sannier, Adrian (Committee member) / Arizona State University (Publisher)
Created2021
Description
Over 7 million students in the US choosing virtual education as they pursue their degree (U.S. Department of Education, 2021). With almost 10,000 business degrees offered online (GetEducated, 2021) digital classes now have to deliver meaningful learning experiences to prepare leaders for inherently relational challenges. This study examines how well online undergraduate students learned and connected in a 7.5-week leadership development course that used a peer coaching model. In this course design, two peer coaches met each week to process and provide feedback on the coursework. Experiential Learning Theory (ELT) suggests that learning is an individual transformation that occurs as learners move through four dialectically opposed learning modes: concrete experience, reflective observation, abstract conceptualization, and active experimentation (Kolb & Kolb, 2017). Learners make meaning of their experience (like conversations or coursework) by thinking about them and developing a mental model that influences their actions which changes the way they view new experiences. In this study, I illustrate how peer coaching supports this transformative process and can help learners expand their thinking not just academically, but personally and professionally too. Moreover, peer coaches emphasize diversity by acknowledging and leveraging markedly different mental models to enhance students’ depth of learning and relating.
I used a convergent mixed-methods design in which qualitative and quantitative data were collected in parallel, analyzed separately and then merged. The reason for collecting both quantitative and qualitative data is to develop a better understanding of the effects of learning preference and affect because each type of data will provide different pieces of evidence regarding those effects. The quantitative data was collected using Qualtrics from self-report surveys using primarily Likert scales to measure learning outcomes, learning preferences, and affect as a part of class exercises. The qualitative data was collected from students’ open-ended reflection assignments about the benefits of differences in their peer coaches. The multiple regressions did not show that learning preference contrasts significantly predicted learning outcomes nor relationships. In contrast, positive affect did predict learning outcomes. The thematic analysis offered clues as to how positive affect improves both learning outcomes and the quality of the peer coaching relationship.
ContributorsReed, Rachel M (Author) / Trinh, Mai P (Thesis advisor) / Foulger, Teresa (Committee member) / Scholar, Brent (Committee member) / Arizona State University (Publisher)
Created2021
Description
Due to the internet being in its infancy, there is no consensus regarding policy approaches that various countries have taken. These policies range from strict government control to liberal access to the internet which makes protecting individual private data difficult. There are too many loopholes and various forms of policy on how to approach protecting data. There must be effort by both the individual, government, and private entities by using theoretical mixed methods to approach protecting oneself properly online.
ContributorsPeralta, Christina A (Author) / Scheall, Scott (Thesis advisor) / Hollinger, Keith (Thesis advisor) / Alozie, Nicholas (Committee member) / Arizona State University (Publisher)
Created2023