Free Productive Ability and Lexical Text Analysis to Improve Student Writing

The classroom is often an arena of Controlled Productive Ability. Within this system, the teacher issues communiques and makes deposits which the students patiently receive, memorize, and repeat. Further, this ‘banking’ concept of education, extends the scope of action afforded to students only as far as receiving, filing, and storing the deposits. Education is thus seen as a process of depositing knowledge into passive students. Freire (1970) exhorts that ‘…the more completely they (the students) accept the passive role imposed on them, the more they tend simply to adapt to the world as it is and to the fragmented view of reality deposited on them’. This research paper will look at how a class of low-intermediate Japanese learners of English, can become more attuned to Free Productive Ability, the active use of productive vocabulary, in their written English endeavors. Writing itself is a production skill, in that it requires learners to produce language, as with speaking activities. Written English can be used to produce a message that you want others to understand. However, at most stages of the writing process from selecting themes and topics, brainstorming ideas, organizing ideas, drafting a text, reviewing and editing before submission, and finally grading and reflecting, the student is part of a passive process managed by the authority of the teacher. This inhibits student critical thinking and the ownership of their own productive abilities. An alternative is to develop and practice a free productive system, limiting the traditional teacher-centric learning system. At all times, students should be encouraged to think, and tackle problems presented to them on their own. This research builds on previous research of student self-affirmation (Deadman, 2015a, 2015b, 2016a and 2016b)

Measurement and effects of teaching quality : an empirical model applied to masters programs

This study applies service quality and customer satisfaction theory to the field of education, and particularly to postgraduate studies. It examines the impact of multiple indicators of teaching quality on student satisfaction. For this purpose, a model is proposed and verified in which the teaching quality indicators are antecedents of the student's satisfaction with the professor and the program. An innovative aspect of the study is the introduction into education of the concept of customer loyalty as a result of satisfaction. In its analysis of these aspects, the study draws on data from a survey conducted among students of two business administration programs. A total of 2,446 valid questionnaires were obtained. In the proposed model, the latent variable, student satisfaction, is considered to be a consequence of the combined effect of satisfaction with certain aspects of teaching quality and the cause of the variation in the indicators on the satisfaction measurement scale. The model was tested by using the MIMIC [Multiple Indicators and Multiple Causes] structural equation technique

Measure for Measure: A Critical Consumers' Guide to Reading Comprehension Assessments for Adolescents

A companion report to Carnegie's Time to Act, analyzes and rates commonly used reading comprehension tests for various elements and purposes. Outlines trends in types of questions, stress on critical thinking, and screening or diagnostic functions

Trajectories of university adjustment in the United Kingdom: Emotion management and emotional self-efficacy protect against initial poor adjustment

Little is known about individual differences in the pattern of university adjustment. This study explored longitudinal associations between emotional self-efficacy, emotion management, university adjustment, and academic achievement in a sample of first year undergraduates in the United Kingdom (N=331). Students completed measures of adjustment to university at three points during their first year at university. Latent Growth Mixture Modeling identified four trajectories of adjustment: (1) low, stable adjustment, (2) medium, stable adjustment, (3) high, stable adjustment, and (4) low, increasing adjustment. Membership of the low, stable adjustment group was predicted by low emotional self-efficacy and low emotion management scores, measured at entry into university. This group also had increased odds of poor academic achievement, even when grade at entry to university was controlled. Students who increased in adjustment had high levels of emotion management and emotional self-efficacy, which helped adaptation. These findings have implications for intervention

Predicting Student Failure in an Introductory Programming Course with Multiple Back-Propagation

One of the most challenging tasks in computer science and similar courses consists of both teaching and learning computer programming. Usually this requires a great deal of work, dedication, and motivation from both teachers and students. Accordingly, ever since the first programming languages emerged, the problems inherent to programming teaching and learning have been studied and investigated. The theme is very serious, not only for the important concepts underlying computer science courses but also for reducing the lack of motivation, failure, and abandonment that result from students frustration. Therefore, early identification of potential problems and immediate response is a fundamental aspect to avoid student’s failure and reduce dropout rates. In this paper, we propose a machine-learning (neural network) predictive model of student failure based on the student profile, which is built throughout programming classes by continuously monitoring and evaluating student activities. The resulting model allows teachers to early identify students that are more likely to fail, allowing them to devote more time to those students and try novel strategies to improve their programming skills

ACADEMIC PERFORMANCE PROFILES: A DESCRIPTIVE MODEL BASED ON DATA MINING

Academic performance is a critical factor considering that poor academic performance is often associated with a high attrition rate. This has been observed in subjects of the first level of Information Systems Engineering career (ISI) of the National Technological University, Resistencia Regional Faculty (UTN-FRRe), situated in Resistencia city, province of Chaco, Argentine. Among them is Algorithms and Data Structures, where the poor academic performance is observed at very high rates (between 60% and about 80% in recent years). In this paper, we propose the use of data mining techniques on performance information for students of the subject mentioned, in order to characterize the profiles of successful students (good academic performance) and those that are not (poor performance). In the future, the determination of these profiles would allow us to define specific actions to reverse poor academic performance, once detected the variables associated with it. This article describes the data models and data mining used and the main results are also commented