Integrative Motivation as a Predictor of Achievement in the Foreign Language Classroom

This study examines the relationship among five independent variables—integrative motivation, instrumental motivation, the need to fulfill a foreign language requirement, grade point average (GPA), and previous years studying Spanish—as predictors of five dependent variables: scores on a simulated oral proficiency interview (SOPI), final exam grades, final grades, the desire to enroll in Spanish courses after completing the language requirement, and intention to major in Spanish. Data from a questionnaire and a SOPI administered to 130 students enrolled in fourth-semester Spanish identified integrative motivation as a significant predictor of SOPI scores and final exam grades. Furthermore, integrative motivation was a significant predictor of students’ desire to enroll in additional coursework after completing the four-semester foreign language requirement. It also had an important role in students’ intention to major in the language. A negative relationship was found between the need to fulfill the language requirement and intent to continue with further studies in Spanish. The findings demonstrate that integrative motivation is important in predicting student achievement in the foreign language classroom

Optimal Weighting for Exam Composition

A problem faced by many instructors is that of designing exams that accurately assess the abilities of the students. Typically these exams are prepared several days in advance, and generic question scores are used based on rough approximation of the question difficulty and length. For example, for a recent class taught by the author, there were 30 multiple choice questions worth 3 points, 15 true/false with explanation questions worth 4 points, and 5 analytical exercises worth 10 points. We describe a novel framework where algorithms from machine learning are used to modify the exam question weights in order to optimize the exam scores, using the overall class grade as a proxy for a student's true ability. We show that significant error reduction can be obtained by our approach over standard weighting schemes, and we make several new observations regarding the properties of the "good" and "bad" exam questions that can have impact on the design of improved future evaluation methods

Enrollment, Attendance and Engagement → Achievement: Successful Strategies for Motivating Students - Evidence of Effectiveness from Comparisons of 50 States and 45 Nations

The purpose of the educational enterprise is LEARNING. Engagement is essential to achieving this purpose. How do we increase the proportion of our young people who enroll in and attend school while simultaneously setting high standards and inducing them to become engaged and effective learners? This paper proposes an agenda of reform to achieve these two goals. Each of proposal has a research literature behind it that makes a good case that the policy simultaneously raises the achievement of existing students and encourages them to stay in school or alternatively achieves one of these goals without sacrificing the other. Strategy # 1 says “Do a better job of convincing adolescents that learning and schooling pays off big time.” Strategy # 2 proposes a variety of ways of making secondary schools both more attractive and more effective. Expand the offerings of and access to career-technical education. Stop building large high schools. Create a new set of small high quality schools of choice: KIPP Academies and Career Academies. In Strategy # 3 I propose that end-of-course exams [not minimum competency exams or standards based exams] be the primary mechanism (along with teacher grades) for signaling student achievements to colleges and employers and for holding high schools accountable.High quality end-of-course exams that reliably measure achievement over the entire A to F range would need to be developed. Exam grades would appear on the student’s transcript, be part of the final grade in the course and be factored into college admissions and placement decisions. The exam would be a spur for everyone in the class to try harder, not just those who are struggling to pass the course. This strategy brings the interests of students, parents and teachers into alignment, encourages a pro-learning culture in the classroom and makes it easier for teachers to be rigorous and demanding. Universal curriculum-based external exam systems—as they are called--work remarkably well in Europe, Canada, North Carolina and New York and there is every reason to expect them to be equally successful when implemented in other SREB states

A Multi-Gene Genetic Programming Application for Predicting Students Failure at School

Several efforts to predict student failure rate (SFR) at school accurately still remains a core problem area faced by many in the educational sector. The procedure for forecasting SFR are rigid and most often times require data scaling or conversion into binary form such as is the case of the logistic model which may lead to lose of information and effect size attenuation. Also, the high number of factors, incomplete and unbalanced dataset, and black boxing issues as in Artificial Neural Networks and Fuzzy logic systems exposes the need for more efficient tools. Currently the application of Genetic Programming (GP) holds great promises and has produced tremendous positive results in different sectors. In this regard, this study developed GPSFARPS, a software application to provide a robust solution to the prediction of SFR using an evolutionary algorithm known as multi-gene genetic programming. The approach is validated by feeding a testing data set to the evolved GP models. Result obtained from GPSFARPS simulations show its unique ability to evolve a suitable failure rate expression with a fast convergence at 30 generations from a maximum specified generation of 500. The multi-gene system was also able to minimize the evolved model expression and accurately predict student failure rate using a subset of the original expressionComment: 14 pages, 9 figures, Journal paper. arXiv admin note: text overlap with arXiv:1403.0623 by other author

Diplomas for Learning, not Seat Time: The Impacts of New York Regents Examinations

[Excerpt] New York State has been administering curriculum-based Regents Examinations to high school students ever since June 1878. As Sherman Tinkelman, Assistant Commissioner for Examinations and Scholarships described in a 1966 report: The Regents examinations are closely related to the curriculum in New York State. They are, as you can see, inseparably intertwined. One supports and reinforces the other.... These instruments presuppose and define standards.... They are a strong supervisory and instructional tool-- and deliberately so. They are effective in stimulating good teaching and good learning practices (Tinkelman, 1966 p. 12)

Stability and sensitivity of Learning Analytics based prediction models

Learning analytics seek to enhance the learning processes through systematic measurements of learning related data and to provide informative feedback to learners and educators. Track data from Learning Management Systems (LMS) constitute a main data source for learning analytics. This empirical contribution provides an application of Buckingham Shum and Deakin Crick’s theoretical framework of dispositional learning analytics: an infrastructure that combines learning dispositions data with data extracted from computer-assisted, formative assessments and LMSs. In two cohorts of a large introductory quantitative methods module, 2049 students were enrolled in a module based on principles of blended learning, combining face-to-face Problem-Based Learning sessions with e-tutorials. We investigated the predictive power of learning dispositions, outcomes of continuous formative assessments and other system generated data in modelling student performance and their potential to generate informative feedback. Using a dynamic, longitudinal perspective, computer-assisted formative assessments seem to be the best predictor for detecting underperforming students and academic performance, while basic LMS data did not substantially predict learning. If timely feedback is crucial, both use-intensity related track data from e-tutorial systems, and learning dispositions, are valuable sources for feedback generation