Matching Items (6)
Filtering by
- All Subjects: Educational technology
- Genre: Masters Thesis
- Creators: Acuna, Ruben
- Creators: Craig, Scotty
Description
Multimedia educational technologies have increased their presence in traditional and online classrooms over the course of the previous decade. These tools hold value and can promote positive learning outcomes but are reliant on students’ degree of cognitive engagement and self-regulation. When students are not cognitively engaged or have low self-regulation capabilities, their interaction with the technology becomes less impactful because of decreased learning outcomes. Building or altering technologies to cognitively engage students is costly and timely; the present study investigates if introducing higher agency roles, to change the role of the student, increases learning outcomes. Specifically, this study investigates if higher agency roles of a designer or teacher enhances cognitive engagement and improves learning when compared to the conventional role of a learner. Improved learning outcomes were observed from the pretest to posttest for the learner, designer, and teacher role. Participants engaged with higher agency roles did not demonstrate more growth from pretest to posttest when compared to the control group, but participants in the teacher role outperformed those in the designer role. Additionally, reading ability did not impact learning gains across groups. While students who engaged with higher agency roles did not achieve greater learning outcomes than students in the control group, results indicate a learning effect across groups. Results of this study suggest that it was underpowered. Further research is needed to determine the extent of the impact that higher agency roles have on learning outcomes.
ContributorsNovak, Kyrsten (Author) / Roscoe, Rod (Thesis advisor) / Branaghan, Russell (Committee member) / Craig, Scotty (Committee member) / Arizona State University (Publisher)
Created2018
Description
Graph theory is a critical component of computer science and software engineering, with algorithms concerning graph traversal and comprehension powering much of the largest problems in both industry and research. Engineers and researchers often have an accurate view of their target graph, however they struggle to implement a correct, and efficient, search over that graph.
To facilitate rapid, correct, efficient, and intuitive development of graph based solutions we propose a new programming language construct - the search statement. Given a supra-root node, a procedure which determines the children of a given parent node, and optional definitions of the fail-fast acceptance or rejection of a solution, the search statement can conduct a search over any graph or network. Structurally, this statement is modelled after the common switch statement and is put into a largely imperative/procedural context to allow for immediate and intuitive development by most programmers. The Go programming language has been used as a foundation and proof-of-concept of the search statement. A Go compiler is provided which implements this construct.
To facilitate rapid, correct, efficient, and intuitive development of graph based solutions we propose a new programming language construct - the search statement. Given a supra-root node, a procedure which determines the children of a given parent node, and optional definitions of the fail-fast acceptance or rejection of a solution, the search statement can conduct a search over any graph or network. Structurally, this statement is modelled after the common switch statement and is put into a largely imperative/procedural context to allow for immediate and intuitive development by most programmers. The Go programming language has been used as a foundation and proof-of-concept of the search statement. A Go compiler is provided which implements this construct.
ContributorsHenderson, Christopher (Author) / Bansal, Ajay (Thesis advisor) / Lindquist, Timothy (Committee member) / Acuna, Ruben (Committee member) / Arizona State University (Publisher)
Created2018
Description
Learners' attitudes and beliefs during the initial stages of learning have a profound impact on their future decisions, practice habits, and persistence. In music education, however, surprisingly little research has explored how physical equipment design might influence novices' attitudes and beliefs. The current study addresses this gap by examining how novices' motivation and perception differ based on the physical design of the musical instrument they interact with while learning. Fifty-two adult participants completed an online survey measuring their expectancies (e.g., confidence), value beliefs (e.g., enjoyment, interest, and social merit), and anticipated persistence while attempting to learn the electric guitar. Afterward, participants attempted to learn and perform several beginner-level tasks while using a conventionally designed or ergonomically designed guitar. The conventionally designed guitar was a commercially available model marketed toward beginner and intermediate-level guitarists. In contrast, the ergonomic guitar was a custom model based on expert design recommendations to improve ease of use, comfort, and user experience. Participant learning expectations and values were assessed before and after a one-hour practice session. Results revealed that novices who used the ergonomic guitar reported significant gains in anticipated learning enjoyment. Alternatively, novices who used the conventional guitar exhibited no such change. Beyond this relationship however, the ergonomic guitar was not found to meaningfully affect participants' confidence, interest, physical discomfort, and task difficulty perceptions. Additionally, the ergonomic guitar did not have a statistically significant influence on learning persistence ratings. One important implication extracted from this study is that a single practice session may not provide enough time or experience to affect a novices' attitudes and beliefs toward learning. Future studies may seek to remedy this study limitation by using a longitudinal design or longer practice task trials. Despite this limitation however, this exploratory study highlights the need for researchers, music educators, and instrument manufacturers to carefully consider how the physical design of a musical instrument may impact learning attitudes, choices, and persistence over time. Additionally, this study offers the first attempt at extending the equipment design literature to music education and Expectancy-Value Theory.
ContributorsO'Brian, Joseph (Author) / Roscoe, Rod D. (Thesis advisor) / Branaghan, Russell J (Committee member) / Craig, Scotty (Committee member) / Arizona State University (Publisher)
Created2016
Description
Educational technologies can be great tools for learning. The implementation of learning aids and scaffolds within these technologies often make them effective; however, due to various problems, students may take more passive approaches to learning when using these educational tools. This tends to lead to interactions that impair learning. This study approaches this issue by reexamining the learner’s role when interacting with educational technologies. Specifically, the current study attempts to support learning and perceptions by inviting students to approach a learning task like an interface designer or instructional designer. These roles derive from a previous study on higher agency roles. The results of the current study indicate that participants learned across all conditions, suggesting the assignment of roles may not impair learning. However, learning outcomes did not differ between conditions. Additionally, the interface designer and instructional designer roles were more critical of the sounds and organizations of each video than the learner role. Limitations of the study and future directions are discussed.
ContributorsArnold, Samuel Troy (Author) / Roscoe, Rod (Thesis advisor) / Craig, Scotty (Committee member) / Niemczyk, Mary (Committee member) / Arizona State University (Publisher)
Created2020
Description
Plagiarism is a huge problem in a learning environment. In programming classes especially, plagiarism can be hard to detect as source codes' appearance can be easily modified without changing the intent through simple formatting changes or refactoring. There are a number of plagiarism detection tools that attempt to encode knowledge about the programming languages they support in order to better detect obscured duplicates. Many such tools do not support a large number of languages because doing so requires too much code and therefore too much maintenance. It is also difficult to add support for new languages because each language is vastly different syntactically. Tools that are more extensible often do so by reducing the features of a language that are encoded and end up closer to text comparison tools than structurally-aware program analysis tools.
Kitsune attempts to remedy these issues by tying itself to Antlr, a pre-existing language recognition tool with over 200 currently supported languages. In addition, it provides an interface through which generic manipulations can be applied to the parse tree generated by Antlr. As Kitsune relies on language-agnostic structure modifications, it can be adapted with minimal effort to provide plagiarism detection for new languages. Kitsune has been evaluated for 10 of the languages in the Antlr grammar repository with success and could easily be extended to support all of the grammars currently developed by Antlr or future grammars which are developed as new languages are written.
Kitsune attempts to remedy these issues by tying itself to Antlr, a pre-existing language recognition tool with over 200 currently supported languages. In addition, it provides an interface through which generic manipulations can be applied to the parse tree generated by Antlr. As Kitsune relies on language-agnostic structure modifications, it can be adapted with minimal effort to provide plagiarism detection for new languages. Kitsune has been evaluated for 10 of the languages in the Antlr grammar repository with success and could easily be extended to support all of the grammars currently developed by Antlr or future grammars which are developed as new languages are written.
ContributorsMonroe, Zachary Lynn (Author) / Bansal, Ajay (Thesis advisor) / Lindquist, Timothy (Committee member) / Acuna, Ruben (Committee member) / Arizona State University (Publisher)
Created2020
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