Thesis (Ph.D.)--Washington State University, December 2005. / Includes bibliographical references (p. 142-144).

Thesis (Ed. D.)--Illinois State University, 1996. / Title from title page screen, viewed May 23, 2006. Dissertation Committee: Jerry L. Jinks (chair), Paul J. Baker, Norman C. Bettis, Vicky Morgan. Includes bibliographical references (leaves 187-200) and abstract. Also available in print.

Magister Scientiae - MSc / This project discusses whether the British prototype questionnaire called the Assessment Experience Questionnaire (AEQ) could be standardized as a quantifier of assessment and be used at a multicultural institute such as the University of the Western Cape (UWC). This questionnaire was created in the United Kingdom to assist lecturers in evaluating and developing their assessment systems. / South Africa

The problem of this study is to determine the relationships between (1) growth in interest and achievement of high school science students and (2) attitudes, preparation, and experience of science teachers. The study encompasses grades nine through twelve, inclusive, in a sample of Texas accredited public high schools.

This study examines the influence of family background and structure on academic achievement. The research focuses on the 11th- and 12th-grade population in the Texas Academy of Mathematics and Science (TAMS) at the University of North Texas, Denton. The study examines the variables in family background and family structure that contribute to the students' high academic achievement. Twelve hypotheses related to parents, home environment, family structure and interaction, family roles, and family values are proposed. The multivariate analysis shows that the variables being read to, reading independently, fathers' education, mothers' education, and ethnicity are significant in impacting academic achievement. The study underlines the fact that multiple factors in family structure and background have an influence on academic achievement.

No description available.

Degree of Master of Science by research only: A Dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Master of Science. Signed on September 10, 2015 in Johannesburg / The past few years have seen an increase in the number of first year students registering in the School of Computer Science at Wits University. These students come from different backgrounds both academically and socially. As do many other institutions, Wits University collects and stores vast amounts of data about the students they enrol and teach. However this data is not always used after being stored. The area of Educational Data Mining (EDM) focuses on using this stored data to find trends and patterns that could enhance the knowledge about the student’s behavior, their academic performance and the learning environment. This longitudinal study focuses on the application of EDM techniques to obtain a better understanding of some of the factors that influence the academic performance of first year computer science students at the University of the Witwatersrand. Knowledge obtained using these techniques could assist in increasing the number of students who complete their studies successfully and identifying students who are at risk of failing and ensuring that early intervention processes can be put into place. A modified version of the CRISP-DM (CRoss-Industry Standard Process for Data Mining) was used, with three data mining techniques, namely: Classification, Clustering and Association Rule Mining. Three algorithms were compared in the first two techniques while only one algorithm was used in the Association Rule Mining. For the classification technique, the three algorithms that were compared were the J48 Classifier, Decision Table and Na¨ıve Bayes algorithm. The clustering algorithms used included the Simple K-means, Expectation Maximization (EM) and the Farthest First algorithm. Finally, the Predictive Apriori algorithm was selected as the Association Rule Mining technique. Historical Computer Science I data, from 2006 to 2011, was used as the training data. This set of data was used to find relationships within the data that could assist with predictive modeling. For each of the selected techniques a model was created using the training data set. These models were incorporated in a tool, the Success or Failure Determiner (SOFD), that was created specifically as part of this research. Thereafter, the test data set was put through the SOFD tool in the testing phase. Test data sets usually contain a variable whose value is predicted using the models built during the training phase. The 2012 Computer Science I data instances were used during the testing phase. The investigations brought forth both expected and interesting results. A good relationship was found between academic performance in Computer Science and three of the factors investigated: Mathematics I, mid-year mark and the module perceived to be the most difficult in the course. The relationship between Mathematics and Computer Science was expected, However, the other two factors (mid-year mark and most difficult module) are new, and may need to be further investigated in other courses or in future studies. An interesting finding from the Mathematics investigation was the better relationship between Computer Science and Algebra rather than Calculus. Using these three factors to predict Computer Science performance could assist in improving throughput and retention rates by identifying students at risk of failing, before they write their final examinations. The Association Rule Mining technique assisted in identifying the selection of courses that could yield the best academic performance overall, in first year. This finding is important, since the information obtained could be used during the registration process to assist students in making the correct decisions when selecting the courses they would like to do. The overall results show that using data mining techniques and historical data collected atWits University about first year Computer Science (CS-1) students can assist in obtaining meaningful information and knowledge, from which a better unii derstanding of present and future generations of CS-1 students can be derived, and solutions found to some of the academic problems and challenges facing them. Additionally this can assist in obtaining a better understanding of the students and factors that influence their academic performance. This study can be extended to include more courses withinWits University and other higher educational institutions. Keywords. Educational Data Mining, CRISP-DM, Classification, Clustering, Association Rule Mining, J48 Classifier, Decision Table, Na¨ıve Bayes, Simple K-means, Expectation Maximization, Farthest First, Predictive Apriori

This study provides a definition of scientific literacy applicable to secondary school science students. The definition was developed from theories about cognitive processes, the discourse of science, the language register of science and cognitive writing processes. A computer-assisted Scientific Literacy Development Plan was formulated and classroom research undertaken to test its effectiveness. A model of cognitive writing was used as an application of the Scientific Literacy Development Plan in classroom research. The model is called a HyperCard Pathways writing model.The research methodology was a combination of qualitative and quantitative methods and took place in three phases over three academic school years. The HyperCard Pathways model of writing was developed in modules for the topics of the Year 11 Physics Extended Subject Framework of the Senior Secondary Assessment Board of South Australia. Students used the modules for the completion of required pieces of writing in science as part of the requirements for the South Australian Certificate of Education. Results indicated that the Scientific Literacy Development Plan was an effective tool for the enhancement of scientific literacy of Year 11 physics students and there was potential for the use of the plan in other science subjects. A number of teachers incorporated the techniques of the Scientific Literacy Development Plan into their regular course schedules.In conjunction with the classroom research, a method to assess explanation genre essays was developed called the Scientific Explanation Genre Assessment Scheme. This was trialled independently of the trials of the Scientific Literacy Development Plan and was found to be used reliably by teachers of Year 11 physics. The effectiveness of the computer-assisted Scientific Literacy Development Plan was demonstrated by evidence of improvement in scientific ++ / writing beyond that of normal practice. The products of this research: lesson plans, computer discs, and supporting materials were developed to be of assistance to other teachers. The materials can be adapted to other modules in the science curricula, and, following this project some teachers have chosen to do this.

The purpose of this study was to explore the learning strategies that are used by successful science students. In addressing this purpose, a mixed methods approach was adopted in which both quantitative and qualitative methods of data production were used. The participants were both successful and less successful high school science students from grades ten to twelve inclusive. Quantitative data was collected through questionnaires and analysed. The qualitative data was collected through individual semistructured interviews and focus group interviews. This was analysed using a qualitative thematic approach. The research questions were first about the learning strategies that successful science students seemed to use in order to do well academically, and secondly the question of the factors which influenced these successful students. The findings are that there are differences in the use of strategies between the successful students and their less successful counterparts. The successful students in general reported using more learning strategies more often than the less successful students. Successful students also reported that they engaged in strategies for regulating the effort they applied to work on difficult or boring tasks. They engaged more in cognitive strategies that involved deep processing of information, while the less successful students relied more on rehearsal and more superficial strategies like text underlining. Successful students also engaged more in self-regulatory activities that allowed them to monitor and regulate the way they learn. The findings also revealed that the successful students reported that they are influenced in their studies more by such factors as family support, the love of the subject and their goals or ambitions. / Thesis (M.Ed.)-University of KwaZulu-Natal, Durban, 2010.

Academic libraries are increasingly collecting e-books, but little research has investigated how students use e-books compared to print texts. This study used a prompted think-aloud method to gain an understanding of the information retrieval behavior of students in both formats. Qualitative analysis identified themes that will inform instruction and collection practices. / Selinda Adelle Berg, Clinical Medicine Librarian, University of Windsor, Windsor, Ontario, Canada, sberg@uwindsor.ca; Kristin Hoffmann, Head, Research & Instructional Services, The University of Western Ontario, London, Ontario, Canada, khoffma8@uwo.ca; Diane Dawson, Natural Sciences Liaison Librarian, University of Saskatchewan, Saskatoon, Saskatchewan, Canada, diane.dawson@usask.ca