This case study focuses on the implementation and analysis of critical algorithmic literacy (CAL) lessons in two grade 3/4 combination classes. The study involves one elementary school teacher and 36 students from a K-6 school in Southern California. By analyzing various data sources, I identified trends that could be helpful for future…

This report exam­ines how demographics and outcomes changed when schools that had enrolled a dis­pro­por­tion­ate share of the sys­tem's white stu­dents entered the city's cen­tral­ized enroll­ment sys­tem in New Orleans. It finds that the schools enter­ing that sys­tem (OneApp/?NCAP) led to increased access to those schools for Black and oth­er…

Student assignment algorithms have far reaching implications for families, the education system, and for society as a whole. Motivated by operational challenges faced by the San Francisco Unified School District (SFUSD), we use a mechanism design framework to develop, operationalize, and streamline algorithmic student matching policies in San…

The past thirty years have shown a rise in models of language acquisition in which the state of the learner is characterized as a probability distribution over a set of non-stochastic grammars. In recent years, increasingly powerful models have been constructed as earlier models have failed to generalize well to increasingly complex and realistic…

In higher education, significant efforts have been made to improve student success outcomes. In this dissertation, two important problems related to student academic success are considered. The curriculum plays a crucial role in shaping student success. Curricular complexity has been shown to be inversely related to the graduation rate of…

In response to the problem of inaccurate classification of big data information in traditional English teaching ability evaluation algorithms, this paper proposes an English teaching ability estimation algorithm based on big data fuzzy K-means clustering. Firstly, the article establishes a constraint parameter index analysis model. Secondly,…

Artificial intelligence (AI), as an emerging technology, has been widely used in STEM education to promote the educational assessment. Although AI-driven educational assessment has the potential to assess students' learning automatically and reduce the workload of instructors, there is still a lack of review works to holistically examine the field…

Place-based initiatives often use resident surveys to inform and evaluate interventions. Sampling based on well-defined sampling frames is important but challenging for initiatives that target subpopulations. Databases that enumerate total population counts can produce overinclusive sampling frames, resulting in costly outreach to ineligible…

The effectiveness of feedback in enhancing learning outcomes is well documented within Educational Data Mining (EDM). Various prior research have explored methodologies to enhance the effectiveness of feedback to students in various ways. Recent developments in Large Language Models (LLMs) have extended their utility in enhancing automated…

Computational risk detection holds promise for shielding particularly vulnerable groups from online harm. A thorough literature review on real-time computational risk detection methods revealed that most research defined 'real-time' as approaches that analyze content retrospectively as early as possible or as preventive approaches to prevent risks…

The present study was conducted to determine how implementing computational thinking (via a proxy in AP Computer Science Principles) into a school's curriculum impacted student proficiency rates on the New York State Geometry Regents. Recent research has suggested that computational thinking is a skill that transcends specific content areas and…

The field of Data Science has seen rapid growth over the past two decades, with a high demand for people with skills in data analytics, programming, statistics, and ability to visualize, predict from, and otherwise make sense of data. Alongside the rise of various artificial intelligence (AI) and machine learning (ML) applications, we have also…

Higher education is becoming increasingly competitive and all educational institutions are concentrating on improving quality and changing traditional higher education teaching methods. New-type classroom instruction has embraced a unique advancement opportunity with the arrival of the fifth generation (5G) era. It is critical to develop a…

Predicting and improving the academic achievement of university students is a multifactorial problem. Considering the low success rates and high dropout rates, particularly in open education programs characterized by mass enrollment, academic success is an important research area with its causes and consequences. This study aimed to solve a…

In higher education, academic advising is crucial to students' decision-making. Data-driven models can benefit students in making informed decisions by providing insightful recommendations for completing their degrees. To suggest courses for the upcoming semester, various course recommendation models have been proposed in the literature using…