Data mining tool for academic data exploitation: literature review and first architecture proposal

Using data for making decisions is not new; companies use complex computations on customer data for business intelligence or analytics. Business intelligence techniques can discern historical patterns and trends from data and can create models that predict future trends and patterns. Analytics, broadly defined, comprises applied techniques from computer science, mathematics, and statistics for extracting usable information from very large datasets. Data itself is not new. Data has always been generated and used to inform decision-making. However, most of this was structured and organised, through regular data collections, surveys, etc. What is new, with the invention and dominance of the Internet and the expansion of digital systems across all sectors, is the amount of unstructured data we are generating. This is what we call the digital footprint: the traces that individuals leave behind as they interact with their increasingly digital world. Data analytics is the process where data is collected and analysed in order to identify patterns, make predictions, and inform business decisions. Our capacity to perform increasingly sophisticated analytics is changing the way we make predictions and decisions, with huge potential to improve competitive intelligence. These examples suggest that the actions from data mining and analytics are always automatic, but that is less often the case. Educational Data Mining (EDM) and Learning Analytics (LA) have the potential to make visible data that have heretofore gone unseen, unnoticed, and therefore unactionable. To help further the fields and gain value from their practical applications, the recommendations are that educators and administrators: • Develop a culture of using data for making instructional decisions; • Involve IT departments in planning for data collection and use; • Be smart data consumers who ask critical questions about commercial offerings and create demand for the most useful features and uses; • Start with focused areas where data will help, show success, and then expand to new areas; • Communicate with students and parents about where data come from and how the data are used; • Help align state policies with technical requirements for online learning systems. This report documents the first steps conducted within the SPEET1 ERASMUS+ project. It describes the conceptualization of a practical tool for the application of EDM/LA techniques to currently available academic data. The document is also intended to contextualise the use of Big Data within the academic sector, with special emphasis on the role that student profiles and student clustering do have in support tutoring actions. The report describes the promise of educational data mining (seeking patterns in data across many student actions), learning analytics (applying predictive models that provide actionable information), and visual data analytics (interactive displays of analyzed data) and how they might serve the future of personalized learning and the development and continuous improvement of adaptive systems. How might they operate in an adaptive learning system? What inputs and outputs are to be expected? In the next sections, these questions are addressed by giving a system-level view of how data mining and analytics could improve teaching and learning by creating feedback loops. Finally, the proposal of the key elements that conform a software application that is intended to give support to this academic data analysis is presented. Three different key elements are presented: data, algorithms and application architecture. From one side we should have a minimum data available. The corresponding relational data base structure is presented. This basic data can always be complemented with other available data that may help to decide or/and to explain decisions. Classification algorithms are reviewed and is presented how they can be used for the generation of the student clustering problem. A convenient software architecture will act as an umbrella that connects the previous two parts. The document is intended to be useful for a first understanding of academic data analysis. What we can get and what we do need to do. This is the first of a series of reports that taken all together will provide a complete and consistent view towards the inclusion of data mining as a helping hand in the tutoring action.European UnionProgramme: Erasmus+ Project Reference: 2016-1-ES01-KA203-025452info:eu-repo/semantics/draf

An Exploration of Merit Pay, Teacher and Student Satisfaction, and Teacher Performance Evaluation from an Instructional Perspective

Abstract In higher education, teachers’ teaching effectiveness in the classroom is an essential to improve the quality of higher education. However, teachers’ teaching effectiveness comes from the personal motivation, perception, and satisfaction in the teachers’ jobs. The merit incentive compensation system is directly linked to teachers’ motivation and perception, which also directly or indirectly results in satisfaction with teachers’ career and students’ learning in the classroom. This study investigates the factors in teachers’ performance evaluated in Chinese classrooms by students and teachers, and teachers’ demographic characteristics (DC), which impact on teachers’ merit pay, and teachers’/students’ satisfaction. Study participants were Chinese students and teachers working in or enrolled in one of four different higher education systems from 2012 to 2013 semesters in Nanjing, China. Our sample contains 457 teachers and 9,017 students. The data was collected via online questionnaires. Henschke’s Modified Instructional Perspectives Inventory (MIPI) (Henschke, 1989) was used to evaluate teachers’ performance in the classroom from instructional perspectives. The MIPI includes seven factors: Factor 1: Teacher Empathy with Students; Factor 2: Teacher Trust of Students; Factor 3: Planning and Delivery of Instruction; Factor 4: Accommodating Student Uniqueness; Factor 5: Teacher Insensitivity toward Students; Factor 6: Experience-Based Learning Techniques (Learner-Centered Learning Process); and Factor 7: Teacher-Centered Learning Process. The MIPI-s, an adaptation of the MIPI, was used to evaluate student’s perceptions of teacher performance in the classroom from an instructional perspective. Students and teachers reported satisfaction with learning and teaching using a Likert-type scale in a demographic questionnaire. This study utilized a quantitative approach with standard multiple regression analysis. There were three dependent variables: teachers’ merit pay, teachers’ satisfaction, and students’ satisfaction. The independent variables included DC factors related to teachers’ motivation and perception, and seven factors of MIPI and MIPI-s with 45-items respectively. The results of regression analyses demonstrated significant relationships as a whole between teachers’ merit pay and teachers’/students’ satisfaction with teaching/learning, factors in teachers’ demographic characteristics, and seven factors of MIPI/MIPI-S respectively

Use of automated coding methods to assess motivational behaviour in education

Teachers’ motivational behaviour is related to important student outcomes. Assessing teachers’ motivational behaviour has been helpful to improve teaching quality and enhance student outcomes. However, researchers in educational psychology have relied on self-report or observer ratings. These methods face limitations on accurately and reliably assessing teachers’ motivational behaviour; thus restricting the pace and scale of conducting research. One potential method to overcome these restrictions is automated coding methods. These methods are capable of analysing behaviour at a large scale with less time and at low costs. In this thesis, I conducted three studies to examine the applications of an automated coding method to assess teacher motivational behaviours. First, I systematically reviewed the applications of automated coding methods used to analyse helping professionals’ interpersonal interactions using their verbal behaviour. The findings showed that automated coding methods were used in psychotherapy to predict the codes of a well-developed behavioural coding measure, in medical settings to predict conversation patterns or topics, and in education to predict simple concepts, such as the number of open/closed questions or class activity type (e.g., group work or teacher lecturing). In certain circumstances, these models achieved near human level performance. However, few studies adhered to best-practice machine learning guidelines. Second, I developed a dictionary of teachers’ motivational phrases and used it to automatically assess teachers’ motivating and de-motivating behaviours. Results showed that the dictionary ratings of teacher need support achieved a strong correlation with observer ratings of need support (rfull dictionary = .73). Third, I developed a classification of teachers’ motivational behaviour that would enable more advanced automated coding of teacher behaviours at each utterance level. In this study, I created a classification that includes 57 teacher motivating and de-motivating behaviours that are consistent with self-determination theory. Automatically assessing teachers’ motivational behaviour with automatic coding methods can provide accurate, fast pace, and large scale analysis of teacher motivational behaviour. This could allow for immediate feedback and also development of theoretical frameworks. The findings in this thesis can lead to the improvement of student motivation and other consequent student outcomes

A study into the factors that encourage candidates to apply or discourage them from applying for principal positions in Catholic second level schools in the Republic of Ireland and Northern Ireland

This ex post facto piece of research, conducted in Catholic voluntary secondary schools in the Republic of Ireland, and Catholic grant maintained and voluntary grammar schools in Northern Ireland, is an exploration of contemporary leadership succession challenges. There would seem to be an impending shortage of applicants for school principalship- this research establishes empirical evidence as to the situation in the Irish context, North and South. Research questions include: what personal and work related characteristics of senior teachers predispose them to see certain features of the principalship as attractive or unattractive? What conditions associated with the principalship do senior teachers see as objectionable/attractive, i.e. what are the disincentives/incentives associated with the position? What are the specific career intentions of the respondents with regard to principalship in particular? 326 teacher questionnaires were used for analysis. The self-administered questionnaire consisted of 94 fixed-response items. These are used to identify the perceived disincentives and incentives to applying for principalship. Two open-ended statements invite teachers' personal comments on the factors that would influence their decision to apply or not to apply for school leadership. The qualitative data from these two items was used to nuance the quantitative findings. The fixed response items are preceded by 12 items relating to the personal and work-related characteristics of the respondents, with one item relating to their career aspirations. This study, after providing validation data, provides a necessary overview/profile of the study population. This is prior to the employment of inferential statistical analysis using the logistic regression technique. This method of analysis leads to an exploration of the impact of predictor variables on the outcome variable- career intention, which is dichotomous in nature. Results from the univariate and multivariate logistic regression analysis are presented. The study's research objective is the identification of those common or indeed, divergent factors that impact on senior teachers' decision to apply or not to apply for principalship. Six independent variables remain in the final regression model as having statistical significance in the determination of career intention. These include the age group of the respondents, their highest level of education achieved and the school type in which they work. It also includes their perceived lack of expertise, loss of close relationships and the perceived internal rewards connected with the position. It is concluded that there is association between these variables and career intention which is not accounted for by the covariates

Mastery Motivation and Executive Functions as School Readiness Factors: Enhancement of School Readiness in Kenya

The overall goal of this study is to enhance school readiness assessment in Kenya by developing an easy-to-use tablet-based android app that can support teachers and learners during the assessment of Pre-academic skills, Mastery Motivation (MM) and Executive Functions (EF) in the Kenyan context. We operationalised MM and EF as components of Approaches to Learning (ATL): one of the poorly assessed domains of school readiness. This research was based on the theory of ATL and followed a non-experimental longitudinal research design. One study was a Scoping Review that identified the gap in the literature in the assessment of School Readiness domains using game-like apps. This study formed the basis for developing Finding Out Children's Unique Strengths (FOCUS) app for Kenya following Education Design Research Approach. Two studies tested and evaluated the psychometric properties of the FOCUS app in the Kenyan context. Another two empirical studies focused on adapting the Preschool Dimension of Mastery Questionnaire 18 (DMQ 18) and the Childhood Executive Functioning (CHEXI) to complement the assessment of MM and EF, respectively. In addition, one study addressed the role played by MM and EF on school academic performance. A total of 40 teachers, 497 preschool and 535 grade 1 children were involved in this study. Both parametric and non-parametric statistical analyses were used to analyse the generated data. The FOCUS app, CHEXI and DMQ 18 fit well with the data and exhibited strong psychometric properties, thus being suitable for the Kenyan context. Furthermore, both MM and EF were directly and indirectly, involved in grade one children's academic performance. FOCUS app tasks, pre-academic skills, and number and letter search tasks at preprimary II strongly predicted preschool and grade one academic performance. MM assessed using the FOCUS app as a better predictor of academic performance than the DMQ 18. Interventions to improve MM and EF promise to enhance School Readiness in the Kenyan context. The FOCUS app can greatly complement Kenya School Readiness Test to give teachers and parents a broader spectrum to make correct decisions concerning the child

An investigation into the mathematical education of engineering undergraduates in Australian colleges of advanced education

The context of this study is set principally in the DOCIT* colleges, the most significant subset of the Australian Colleges of Advanced Education in terms of engineering education, where the incidence of failure in mathematics courses amongst engineering undergraduates gives rise to fundamental questions about their mathematical education. Starting with the student himself and his prior preparation at school, the mathematical education of engineers is surveyed in all its aspects. Having established the indispensable groundwork that mathematics and mathematical modelling form in engineering education, attention is directed to the aims, objectives and underlying philosophy of mathematical education. The implications for content and methods of teaching and examining are considered. The shortcomings in service teaching are signposted and remedies suggested. Examination techniques are critically reviewed. The need for, and the manner of reducing the discrepancies between intention and achievement in teaching mathematics to engineers prescribe the substance of this study. A chapter elucidates the distinctive aims and functions of universities and CAEs, since these inevitably imply a distinctive emphasis in the design and implementation of courses and lend perspective to the issues raised. Quite apart from its great influence on the applications of mathematics, it is shown how the computer in particular and educational technology in general are valuable resources in making mathematics learning more meaningful, stimulating and illuminating, and for providing individualised instruction. The "state of the art" is summarised by a number of surveys. Recommended teaching syllabuses in mathematical methods, numerical analysis and statistics are preceded by the mapping of considerations that should influence the curriculum and its teaching. The study urges a fundamental review of objectives and the methods of achieving them, and its conclusions and recommendations are stated. * DOCIT: Directors of Central Institutes of Technolog