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- Creators: Middleton, James
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
Description
Concept mapping is a tool used in order to visually represent a person's understanding of interrelated concepts. Generally the central concept is in the center or at the top and the related concepts branch off, becoming more detailed as it continues. Additionally, links between different branches show how those concepts are related to each other. Concept mapping can be implemented in many different types of classrooms because it can be easily adjusted for the needs of the teacher and class specifically. The goal of this project is to analyze both the attitude and achievement of students using concept mapping of college students in an active learning classroom. In order to evaluate the students' concept maps we will use the expert map scoring method, which compares the students concept maps to an expertly created concept map for similarities; the more similar the two maps are, the higher the score. We will collect and record students' scores on concept maps as they continue through the one semester class. Certain chapters correspond to specific exams due to the information contained in the lectures, chapters 1-4 correspond to exam 1 and so forth. We will use this information to correlate the average concept map score across these chapters to one exam score. There was no significant correlation found between the exam grades and the corresponding scores on the concept maps (Pearson's R values of 0.27, 0.26, and -0.082 for Exam 1, 2 and 3 respectively). According to Holm et all "it was found that 85% of students found interest or attainment in the concept mapping session, only 44% thought there was a cost, and 63% thought it would help them to be successful."
ContributorsFarrell, Carilee Dawn (Author) / Ankeny, Casey (Thesis director) / Middleton, James (Committee member) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Advances in computational processing have made big data analysis in fields like music information retrieval (MIR) possible. Through MIR techniques researchers have been able to study information on a song, its musical parameters, the metadata generated by the song's listeners, and contextual data regarding the artists and listeners (Schedl, 2014). MIR research techniques have been applied within the field of music and emotions research to help analyze the correlative properties between the music information and the emotional output. By pairing methods within music and emotions research with the analysis of the musical features extracted through MIR, researchers have developed predictive models for emotions within a musical piece. This research has increased our understanding of the correlative properties of certain musical features like pitch, timbre, rhythm, dynamics, mel frequency cepstral coefficients (MFCC's), and others, to the emotions evoked by music (Lartillot 2008; Schedl 2014) This understanding of the correlative properties has enabled researchers to generate predictive models of emotion within music based on listeners' emotional response to it. However, robust models that account for a user's individualized emotional experience and the semantic nuances of emotional categorization have eluded the research community (London, 2001). To address these two main issues, more advanced analytical methods have been employed. In this article we will look at two of these more advanced analytical methods, machine learning algorithms and deep learning techniques, and discuss the effect that they have had on music and emotions research (Murthy, 2018). Current trends within MIR research, the application of support vector machines and neural networks, will also be assessed to explain how these methods help to address the two main issues within music and emotion research. Finally, future research within the field of machine and deep learning will be postulated to show how individuate models may be developed from a user or a pool of user's listening libraries. Also how developments of semi-supervised classification models that assess categorization by cluster instead of by nominal data, may be helpful in addressing the nuances of emotional categorization.
ContributorsMcgeehon, Timothy Makoto (Author) / Middleton, James (Thesis director) / Knowles, Kristina (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-12