This chapter uses critical perspectives on whiteness to critique higher education's institutional research practice. After briefly describing institutional research, we summarize scholarship about autonomy, ethics, and predictive analytics to illustrate how existing guidance and beliefs about institutional research often dehumanize students by…

Predictive analytics is widely used in various domains, including education, to inform decision-making and improve outcomes. However, many predictive models are proprietary and inaccessible for evaluation or modification by researchers and practitioners, limiting their accountability and ethical design. Moreover, predictive models are often opaque…

Choosing a field of study (study major) is challenging for prospective students. However, little research has examined factors measured prior to enrollment to predict motivation and well-being in a specific study major. Based on literature on affective forecasting and person-environment fit, prospective students' well-being forecast could be such…

Since the introduction by Kermack and McKendrick in 1927, the Susceptible-Infected-Recovered (SIR) epidemic model has been a foundational model to comprehend and predict the dynamics of infectious diseases. Almost for a century, the SIR model has been modified and extended to meet the needs of different characteristics of various infectious…

It is no surprise that concern for the future is on the rise. Several catastrophes obscure our future(s) imaginary, such as climate change, a global pandemic, racial inequality, and political polarization. Students are feeling a disconnect between what they learn in classrooms and the futures that populate their media platforms. Futures literacies…

Many studies have linked quality in early childhood education and care [ECEC] to school performance, but the mechanisms of how ECEC process quality affects children in ways that lead to improved school performance is unclear. In this study on 7431 children in Norway, we test the hypothesis that the relation between process quality in ECEC and…

This study investigates infants' neural and behavioral responses to maternal ostensive signals during naturalistic mother-infant interactions and their effects on object encoding. Mothers familiarized their 9- to 10-month-olds (N = 35, 17 females, mainly White, data collection: 2018-2019) with objects with or without mutual gaze, infant-directed…

Knowledge tracing (KT) for evaluating students' knowledge is an essential task in personalized education. More and more researchers have devoted themselves to solving KT tasks, e.g., deep knowledge tracing (DKT), which can capture more sophisticated representations of student knowledge. Nonetheless, these techniques ignore the reconstruction of…

Given the extensive use of online learning in educational settings, Knowledge Tracing (KT) is becoming increasingly essential. KT primarily aims to predict a student's future knowledge acquisition based on their past learning activities, thus enhancing the efficiency of student learning. However, the effective acquisition of dynamic and evolving…

Higher education practitioners and researchers in the STEM field continue seeking ways to effectively identify and understand student challenges as part of an effort to support student success, retention, and persistence. These efforts have led researchers to explore non-cognitive personality factors such as perfectionism as a way of understanding…

Designing and constructing pedagogical tests that contain items (i.e. questions) which measure various types of skills for different levels of students equitably is a challenging task. Teachers and item writers alike need to ensure that the quality of assessment materials is consistent, if student evaluations are to be objective and effective.…

Prediction error plays a pivotal role in theories of learning, including theories of language acquisition and use. Researchers have investigated whether and under which conditions children, like adults, use prediction to facilitate language comprehension at different levels of linguistic representation. However, many aspects of the reciprocal…

Social desirability bias (SDB) is a common threat to the validity of conclusions from responses to a scale or survey. There is a wide range of person-fit statistics in the literature that can be employed to detect SDB. In addition, machine learning classifiers, such as logistic regression and random forest, have the potential to distinguish…

In every culture and era, education is considered the most fundamental reality and rule that societies prioritize and deem essential. Throughout the process spanning thousands of years, from the emergence of writing to the present day, education has undergone various forms and formats of change. Education has been a continuous guide for shaping,…

This dissertation examined classification outcome differences among four popular individual supervised machine learning (ISML) models (logistic regression, decision tree, support vector machine, and multilayer perceptron) when predicting minor class membership within imbalanced datasets. The study context and the theoretical population sampled…