Giving All Children a Good Start to School: A Protocol in Defining Occupational Therapist\u27s Role in School Readiness, Specifically in the Smart Program

More children are entering school not ready. Current research is showing that many healthy children are entering school not developmentally ready for formal learning. Based my own experiences working as a school occupational therapist for the past four years in rural area schools, this new trend is becoming more apparent. Teachers and other school professionals have increasing concerns over the numbers of children that seem clumsy or awkward compared to their peers. Children are expected to learn more than ever before upon entering school, the curriculum is no longer age-appropriate due to the pushing down of academics. Upon entrance to kindergarten, these children struggle to perform school tasks asked of them and the concern is if the readiness gaps are not addressed early on that these children will struggle even more in later years. It has been found through the literature and from personal experience that children in Title I schools are particularly vulnerable to lack readiness because of cultural, environmental, and/or economic deprivation. A current study by the Minnesota Department of Education in 2004, found that 28% of 4,000 children assessed in Minnesota kindergartens performed skills at a level that made them not ready for academic learning in kindergarten. In response to these concerns and findings, a program that has caught the attention of many educators and school occupational therapists is the SMART (Stimulating Maturity through Accelerated Readiness Training) Program. This program is being used in many Minnesota schools in order to close school readiness gaps and give children a good start to school and future school success. The program so closely parallels the role of school occupational therapists that many have become very involved in the program. The purpose of this project was to closely examine the SMART program and to determine how occupational therapists can help implement and promote the use of this program. The product of this project was the development of a protocol, consisting of 10 modules, that occupational therapists can use to facilitate involvement in and promotion of the SMART Program to help address school readiness issues

Teachers\u27 Conceptions of Mathematics and Intelligent Tutoring System Use

The purpose of this mixed-methods study was to investigate the relationship between teachers’ conceptions of mathematics and their use of intelligent tutoring systems for mathematics instruction. Intelligent tutoring systems are adaptive computer programs which administer mathematics instruction to students based on their cognitive state. A conception is a mixture of beliefs and knowledge. The participants in this study were 93 junior high school mathematics teachers from three school districts in the Midwest. Data were gathered using a two-part online survey. The first part of the survey contained questions about their use of intelligent tutoring systems, graphing calculators, Desmos and dynamic geometry software. The second part of the survey contained Likert questions from the teachers’ version of the Conceptions of Mathematics Inventory. Desmos is a website providing interactive classroom activities and a user-friendly graphing calculator. Dynamic geometry software is a class of interactive geometry programs. The quantitative analysis revealed no statistically significant interactions between teachers’ conception scores and intelligent tutoring system use, or between teachers’ conception scores and how intelligent tutoring systems were used. There were statistically significant interactions between teachers’ conception scores and their use of graphing calculators, Desmos, and dynamic geometry software. The qualitative analysis revealed that teachers used intelligent tutoring systems for differentiation. Teachers used graphing calculators, Desmos, and dynamic geometry software for visual, computational, and exploratory purposes. Teachers exclusively using intelligent tutoring systems to incorporate technology should also incorporate technology which promotes student exploration

Assessing Mechanistic Reasoning: Supporting Systems Tracing

Reasoning about mechanism is central to disciplined inquiry in science and engineering and should thus be one of the foundations of a science, technology, engineering, and mathematics education. In addition, mechanistic reasoning is one of the core competencies listed in the Next Generation Science Standards (NGSS) Engineering Concepts and Practices (NGSS Lead States, 2013). Mechanistic explanations focus on the processes that underlie cause–effect relationships and consider how the activities of system components affect one another. While some assessment work has been accomplished in engineering education, to date mechanistic reasoning is an area where limited assessment development has been accomplished for pre-college populations. The data in this study come from the calibration of the Assessment of Mechanistic Reasoning Project (AMRP) (Weinberg, 2012), designed to diagnose individuals’ mechanistic reasoning about systems of levers. This assessment presents a domain-specific characterization of mechanistic reasoning and illuminates what is easy and difficult about this form of reasoning. The study participants included elementary, middle, and high school students as well as college undergraduates and adults without higher education. Within this calibration study, item analyses, reliability, and validity measures were conducted using item response theory modeling; the AMRP assessment was found to have high reliability and validity. In addition, this study shows that machine characteristics such as number of levers, lever type, and arrangement of levers can affect the difficulty of mechanistic reasoning

A Causal-Comparative Study on the Efficacy of Intelligent Tutoring Systems on Middle-Grade Math Achievement

This study is a quantitative examination of intelligent tutoring systems in two similar suburban middle schools (grades 6-8) in the Southeastern United States. More specifically, it is a causal-comparative study purposed with examining the efficacy of intelligent tutoring systems as they relate to math achievement for students at two similar middle schools in the Midlands of South Carolina. The independent variable, use of an intelligent tutoring system in math instruction, is defined as the supplementary use of two intelligent tutoring systems, Pearson’s Math Digits and IXL, for math instruction. The dependent variable is math achievement as determined by the Measures of Academic Progress (MAP) SC 6+Math test. The student data examined is archived MAP SC 6+ Math scores from the 2017-2018 school year. A one-way ANCOVA was used to compare the mean achievement gain scores of both groups, students whose math instruction included intelligent tutoring systems and students whose math instruction did not include intelligent tutoring systems, to establish whether or not there was any statistically significant difference between the adjusted population means of the two independent groups. The results showed that the adjusted mean of posttest scores of students who did not receive math instruction that involved an intelligent tutoring system were significantly higher than those who did

Implementing citizen science programmes in the context of university gardens to promote preservice teachers' scientific literacy: a study case on soil

This work presents an assessment of pre-service teachers' argumentative practice, after implementing a novel teaching-learning sequence on soil health including a citizen science programme, which was applied outdoors at the university garden. The sequence was implemented at five Spanish universities with 351 undergraduates studying Early Childhood and Primary Teacher Education. It posed a final assessment task consisting in a real-world situation that involved making decisions on science-related issues: students needed to argue whether it was possible to use a piece of land as a school garden, based on soil data provided in a variety of formats. To assess participants' level of achievement, a rubric was specifically designed by adapting the Evidence-Explanation Continuum approach, which was applied to a subsample of 123 answers (35%). Results evidenced that the process of knowledge-building discourse from initial data to final explanations involved a series of transformations of increasing difficulty, since the percentage of students who were able to correctly accomplish them decreased a long the continuum. Including the citizen science programme promoted the development of basic aspects of scientific literacy related to interpreting data and evidence scientifically but, for students to be generally capable of drawing evidence-based conclusions, argumentation practices should be regularly included in science classes

Improving Phonics and Fluency Skills Using a Multisensory Language Intervention

The purpose of this single case design study was to examine the efficacy of multisensory language instruction, specifically the Wilson Reading System, on the phonetic analysis skills and reading fluency of a single student identified as dyslexic. Data was collected for a period of eight weeks and analyzed using visual representations to determine participant growth in all areas assessed. Data showed growth of 20 words read correctly per minute (WCPM) with a projected gain of 16 WCPM according to Hasbrouk and Tindal\u27s (2006) reading fluency normative chart. The education implications of the study are discussed and recommendations for further research are given. Overall, the intervention was deemed successful from data collected

Graduate Catalog, 1990-1991

https://scholar.valpo.edu/gradcatalogs/1018/thumbnail.jp

Characterizing Productive Perseverance Using Sensor-Free Detectors of Student Knowledge, Behavior, and Affect

Failure is a necessary step in the process of learning. For this reason, there has been a myriad of research dedicated to the study of student perseverance in the presence of failure, leading to several commonly-cited theories and frameworks to characterize productive and unproductive representations of the construct of persistence. While researchers are in agreement that it is important for students to persist when struggling to learn new material, there can be both positive and negative aspects of persistence. What is it, then, that separates productive from unproductive persistence? The purpose of this work is to address this question through the development, extension, and study of data-driven models of student affect, behavior, and knowledge. The increased adoption of computer-based learning platforms in real classrooms has led to unique opportunities to study student learning at both fine levels of granularity and longitudinally at scale. Prior work has leveraged machine learning methods, existing learning theory, and previous education research to explore various aspects of student learning. These include the development of sensor-free detectors that utilize only the student interaction data collected through such learning platforms. Building off of the considerable amount of prior research, this work employs state-of-the-art machine learning methods in conjunction with the large scale granular data collected by computer-based learning platforms in alignment with three goals. First, this work focuses on the development of student models that study learning through the use of advancements in student modeling and deep learning methodologies. Second, this dissertation explores the development of tools that incorporate such models to support teachers in taking action in real classrooms to promote productive approaches to learning. Finally, this work aims to complete the loop in utilizing these detector models to better understand the underlying constructs that are being measured through their application and their connection to productive perseverance and commonly-observed learning outcomes

Graduate Catalog, 2005-2006

https://scholar.valpo.edu/gradcatalogs/1032/thumbnail.jp