AUTISM AND SELF-DETERMINATION: MEASUREMENT AND CONTRAST WITH OTHER DISABILITY GROUPS

This dissertation consists of four chapters. Chapter 1 provides an introduction to the self-determination literature documenting the importance of promoting the self-determination of transition and secondary age students with disabilities, as well as a summary of research examining the self-determination of students with disabilities across disability categories, with a particular focus on students with autism spectrum disorders (ASD) and the need for additional research with this latter population. Chapter 2 investigates the factor structures of two instruments measuring the self-determination of students with ASD. Ninety-five middle and high school students (17% female and 83% male) ages 13 through 22 years participated in the investigation of the validity of two instruments, The Arc's Self-Determination Scale (SDS) and AIR Self-Determination Scale (AIR). A Confirmatory Factor Analysis (CFA) was conducted separately for the SDS and AIR data. The findings of this study indicated that the parameter estimates and the model fit results supported the hypothesized factor structure in this sample, at least for the first three of four factors of the SDS and fully supported the two factors of the AIR. Chapter 3 builds on the findings of Chapter 2 and examines the differences in self-determination among students with ASD, students with intellectual disability (ID), and students with learning disabilities (LD). A total of 222 participants with an equal size group for each of the three disability categories (ASD, ID, LD) were selected to participate in the comparison of total self-determination and domain scores. One-way between-subjects multivariate analysis of variance (MANOVA) was performed on six dependent variables/factors, including autonomy, self-regulation, psychological empowerment, self-realization, capacity, and opportunity. The results indicated that (a) students with ASD and ID and LD were different in their scores in these domains, and (b) students with ASD had lower levels of autonomy when compared to students with LD. Chapter 4 presents the conclusions and implications of the findings of Chapter 2 and 3. The primary implications for future research indicate that the factors of the two self-determination measures can be used as reliable outcome variables useful for detecting treatment effects of experimental design studies promoting the self-determination of students with ASD. Also, future research is encouraged to investigate the items that loaded negatively onto Self-Realization domain of the SDS. In addition to significant group differences in self-determination among three disability groups, future research should examine group differences in each essential characteristic of self-determination or in the component elements of self-determined behavior to provide a more completed profile of relative self-determination for this group. The primary implications for educators were that the two commonly used instruments are applicable to the population of students with ASD. Also, students with ASD, ID, and LD need instruction to promote self-determination, but students with ASD also need instructional emphases on several component elements as shown by the domain-level differences found in this study

The Computational Lens: from Quantum Physics to Neuroscience

Two transformative waves of computing have redefined the way we approach science. The first wave came with the birth of the digital computer, which enabled scientists to numerically simulate their models and analyze massive datasets. This technological breakthrough led to the emergence of many sub-disciplines bearing the prefix "computational" in their names. Currently, we are in the midst of the second wave, marked by the remarkable advancements in artificial intelligence. From predicting protein structures to classifying galaxies, the scope of its applications is vast, and there can only be more awaiting us on the horizon. While these two waves influence scientific methodology at the instrumental level, in this dissertation, I will present the computational lens in science, aiming at the conceptual level. Specifically, the central thesis posits that computation serves as a convenient and mechanistic language for understanding and analyzing information processing systems, offering the advantages of composability and modularity. This dissertation begins with an illustration of the blueprint of the computational lens, supported by a review of relevant previous work. Subsequently, I will present my own works in quantum physics and neuroscience as concrete examples. In the concluding chapter, I will contemplate the potential of applying the computational lens across various scientific fields, in a way that can provide significant domain insights, and discuss potential future directions.Comment: PhD thesis, Harvard University, Cambridge, Massachusetts, USA. 2023. Some chapters report joint wor