Predicting the academic success of architecture students by pre-enrolment requirement: using machine-learning techniques

In recent years, there has been an increase in the number of applicants seeking admission into architecture programmes. As expected, prior academic performance (also referred to as pre-enrolment requirement) is a major factor considered during the process of selecting applicants. In the present study, machine learning models were used to predict academic success of architecture students based on information provided in prior academic performance. Two modeling techniques, namely K-nearest neighbour (k-NN) and linear discriminant analysis were applied in the study. It was found that K-nearest neighbour (k-NN) outperforms the linear discriminant analysis model in terms of accuracy. In addition, grades obtained in mathematics (at ordinary level examinations) had a significant impact on the academic success of undergraduate architecture students. This paper makes a modest contribution to the ongoing discussion on the relationship between prior academic performance and academic success of undergraduate students by evaluating this proposition. One of the issues that emerges from these findings is that prior academic performance can be used as a predictor of academic success in undergraduate architecture programmes. Overall, the developed k-NN model can serve as a valuable tool during the process of selecting new intakes into undergraduate architecture programmes in Nigeria

Signaling the Competencies of High School Students to Employers

[Excerpt] The fundamental cause of the low effort level of American students, parents, and voters in school elections is the absence of good signals of effort and accomplishment and the consequent lack of rewards for learning. In most other advanced countries mastery of the curriculum is assessed by examinations that are set and graded at the national or regional level. Grades on these exams signal the student\u27s achievement to employers and colleges and influence the jobs that graduates get and the universities and programs to which they are admitted. Exam results also influence school reputations and in some countries the number of students applying for admission to the school. In the United States, by contrast, students take aptitude tests that are not intended to assess the learning that has occurred in most of the classes taken in high school. The primary signals of academic achievement are diplomas awarded for time spent in school and grades and rank in class—criteria that assess achievement relative to other students in the school or classroom, not relative to an external standard

Examining Predictor Measures for Students\u27 Testing in the International Baccalaureate Diploma Programme

The purpose of this study was to explore the relationships between both student achievement and growth in 9th and 10th grade English and math courses and achievement on International Baccalaureate English and math exams. The researcher examined student end-of-course exam scores, student growth values, and International Baccalaureate English and math exam scores from an International Baccalaureate World School across 4 graduating cohorts, including 305 students. The researcher concluded that while 9th grade math end-of-course exam achievement does not significantly predict International Baccalaureate math exam scores, student growth in 9th grade math does. The researcher also concluded that Advanced Placement Language exam scores have a strong statistical relationship to International Baccalaureate English scores and can significantly predict International Baccalaureate English exam scores. Overall, the researcher concluded that both student end-of-course exam scores and student growth are statistically related to, and can predict, success on International Baccalaureate English and math exams

Moving the Needle: Exploring Key Levers to Boost College Readiness Among Black and Latino Males in New York City

Moving the Needle addresses the challenges, opportunities, and potential solutions to increasing college readiness rates for young men of color in New York City. The report describes indicators that help predict college readiness, environmental factors that affect educational outcomes, and how this research can inform the City's Expanded Success Initiative

Behavioral engagement and disaffection in school activities: Exploring a model of motivational facilitators and performance outcomes

Investigaciones previas han mostrado que el control percibido, el valor de la tarea, el compromiso conductual y la desafección son determinantes personales del rendimiento académico. Sin embargo, pocas investigaciones han examinado simultáneamente estos constructos en educación secundaria. El presente estudio analizó las relaciones estructurales entre estas variables y el papel del compromiso y la desafección como mediadores de los efectos del control y el valor sobre el rendimiento. Los participantes fueron 446 estudiantes (51.3% chicas) con edades comprendidas entre 12 y 16 años que asistían a seis colegios de educación secundaria obligatoria (de 7º a 10º cursos; de 1º a 4º de ESO). Las variables se evaluaron a lo largo de nueve meses. Los resultados de los modelos de ecuaciones estructurales confirmaron las hipótesis: el control y el valor predijeron significativamente el compromiso, la desafección y el rendimiento; el compromiso y la desafección predijeron el rendimiento y mediaron parcialmente los efectos del control y el valor sobre el rendimiento. Se concluye discutiendo las implicaciones para la teoría y la práctica psicoeducativa.Previous research has shown that perceived control, task value, behavioral engagement and disaffection are personal determinants of academic performance. However, little research has simultaneously examined these constructs in secondary education. The present study analyzed the structural relationships between these variables and the role of engagement and disaffection as mediators of control and value on performance. Participants were 446 students (51.3% girls) ranging in age from 12 to 16 years attending six Spanish compulsory secondary schools (from 7th to 10th grades). The variables were assessed over a nine-month period. Structural equation models results confirmed the hypotheses: control and value significantly predicted engagement, disaffection, and performance; engagement and disaffection predicted performance and partially mediated the effects from control and value on performance. Implications for psycho-educational theory and practice are discussed.Fil: González, Antonio. Universidad de Vigo; EspañaFil: Paoloni, Paola Veronica Rita. Universidad Nacional de Río Cuarto; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Donolo, Danilo Silvio. Universidad Nacional de Río Cuarto; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Rinaudo, María Cristina. Universidad Nacional de Río Cuarto; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

A Nation Deceived: How Schools Hold Back America's Brightest Students, Volume II

Provides a comprehensive review of research on the academic acceleration of gifted students

A Longitudinal Study of High School Success, College Readiness, and College Success Among High School Students

The purpose of this dissertation was to use the Education Longitudinal Study of 2002 data set, a nationally representative and longitudinal study of high school students, to determine if the combination of Goals and Expectations, Outcomes and Measures, Pathways and Supports, and Resources and Structures strands of the Organizer Model significantly predict high school success, college readiness, and college success among high school students in the United States. Second, this study was to determine if high school success and college readiness significantly mediate the effects of mentioned strands on college success while controlling for demographic, socioeconomic, and geo regional characteristics. The main analytical method for testing the hypotheses was multivariable logistic regression. Components of the theoretical model consistently revealed associations across all hypotheses, and statistical models are variables of Goals and Expectations and Outcomes and Measure strands. The Resources and Structures strand measures seem more relevant for college outcomes. The Pathways and Supports strand was not significant in the fully adjusted models. College readiness was a significant mediator between the model strands and college success, while high school success did not show the same effect. Practitioners should continue to use proven methods to support and sustain student success while transitioning from high school to college

An Investigation of the Combined Assessments Used as Entrance Criteria for a Gifted English Middle School Program

The purpose of this study was to determine if the four assessments for entrance into an academic middle school gifted English program were accurately predicting success, as measured by students’ grades each nine-week grading period. Some students were dismissed from the program each year because they could not maintain the required minimum average of 80%. The four entrance assessments evaluated were the Cognitive Abilities Test (CogAT), Naglieri Nonverbal Ability Test (NNAT), STEP Writing test, and the Iowa Test of Basic Skills: Reading (ITBS). The sample consisted of 150 sixth, seventh, and eighth grade students studied longitudinally over the span of four years from a suburban middle school in a large Texas school district. Using correlation, logistic regression, and generalized linear regression models, the results showed that all the students selected to participate in the middle school gifted English program were statistically capable of success, whether they successfully remained in the program or not. Additionally, the results indicated that the two achievement tests (ITBS Reading and STEP Writing test) better predicted which students were successful, whereas the aptitude tests (CogAT and NNAT) did not. The achievement tests were determined to be better predictors of students’ success, as measured by grades, in this rigorous academic middle school gifted English program. Other findings include (a) students’ grades increased over time in the program, (b) females were predicted to earn about two point higher grades than males, and (c) the individual student was a significant predictor of success based on entrance scores. Finally, several recommendations were made for future research. These possibilities include repeating this study using standard scores for data analysis rather than the percentile scores that were available for this investigation. An additional recommendation is to investigate a possible replacement for the STEP Writing test, as it has not been nationally normed in decades. Another possibility would be to evaluate the curriculum and teacher effectiveness within the district using the NAGC (2010b) Pre-K-Grade 12 Gifted Programming Standards. A final potential study could implement specific interventions for use with students at-risk for underachievement to determine which strategies are most effective

AN INTEGRATED MULTIPLE STATISTICAL TECHNIQUE FOR PREDICTING POST-SECONDARY EDUCATIONAL DEGREE OUTCOMES BASED PRIMARILY ON VARIABLES AVAILABLE IN THE 8TH GRADE

There is a class of complex problems that may be too complicated to solve by any single analytical technique. Such problems involve so many measurements of interconnected factors that analysis with a single dimension technique may improve one aspect of the problem while overall achieving little or no improvement. This research examines the utility of modeling a complex problem with multiple statistical techniques integrated to analyze different types of data. The goal was to determine if this integrated approach was feasible and provided significantly better results than a single statistical technique. An application in engineering education was chosen because of the availability and comprehensiveness of the NELS:88 longitudinal dataset. This dataset provided a huge number of variables and 12,144 records of actual students progressing from 8th grade to their final educational outcomes 12 years later. The probability of earning a Science, Technology, Engineering, or Mathematics (STEM) degree is modeled using variables available in the 8th grade as well as standardized test scores. The variables include demographic, academic performance, and experiential measures. Extensive manipulation of the NELS:88 dataset was conducted to identify the student outcomes, prepare the set of covariates for modeling, and determine when the students' final outcome status occurred. The integrated models combined logistic regression, survival analysis, and Receiver Operating Characteristics (ROC) Curve analysis to predict obtaining a STEM degree vs. other outcomes. The results of the integrated models were compared to actual outcomes and the results of separate logistic regression models. Both sets of models provided good predictive accuracy. The feasibility of integrated models for complex problems was confirmed. The integrated approach provided less variability in incorrect STEM predictions, but the improvement was not statistically significant. The main contribution of this research is designing the integrated model approach and confirming its feasibility. Additional contributions include designing a process to create large multivariate logistic regression models; developing methods for extensive manipulation of a large dataset to adapt it for new analytical purposes; extending the application of logistic regression, survival analysis, and ROC Curve analysis within educational research; and creating a formal definition for STEM that can be statistically verified