This research aims to enhance the culture of data in education which is in the middle of a major transformation by technology and Big Data Analytics. The core purpose of schools is providing an excellent education to every learner; data can be the leverage of that mission. Big data analytics is the process of examining large sets containing a variety of data types to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. Valuable lessons can be learnt from other industries when considered in terms of their practicality for public education. Hence, Big Data Analytics, also known as Education Data Mining and Learning Analytics, develop capacity for quantitative research in response to the growing need for evidence-based analysis related to education policy and practice. However, education has been slow to follow the Data Analytics evolution due to difficulties surrounding what data to collect, how to collect those data and what they might mean. Our research works identify, quantify, and measure the qualitative teaching practices, the learning performances, and track the learners' academic progress. Teaching and learning databases are accumulated from quantitative “measures” done through indoors classroom visits within academic institutions, online web access learners' questionnaires answers, paper written statements' analysis of academic exams in mathematics, science, and literacy disciplines, and online elementary grades seizure from written traces of learners' performances within mathematics, science, and literacy exams. The project's data mining strategy will support and develop teachers' expertise, enhance and scaffold students' learning, improve and raise education system's performance. The supervisor expertise will mentor the researcher for information and educational knowledge extraction from collected data. As consequence, the researcher will acquire the wisdom of knowledge use to translate it into more effective training sessions on educational concrete policies.


Anne Jorro says: “evaluate is necessarily considering how we will support, advise, exchange, to give recognition to encourage the involvement of the actor giving him the means to act”. PISA report states that many of the world's best-performing education systems have moved from bureaucratic “command and control” environments towards school systems in which the people at the frontline have much more control. Making teaching and learning data available leads to information then knowledge extraction. As advised by PISA report, the effective use of extracted knowledge drives decision making to wisdom. Linda Darling-Hammond and Charles E. Ducommun underscore the important assumption that, undoubtedly, teachers are the fulcrum that has the biggest impact and makes any school initiative leads toward success or failure. Rivkin and al ensure that a teacher's classroom instructional practice is perhaps one of the most important yet least understood factors contributing to teacher effectiveness. As consequence, many classroom observation tools are designed to demystify effective teaching practices. The Classroom Assessment Scoring System is a well-respected classroom climate observational system. The CLASS examines three domains of behaviour including, firstly, emotional support (positive classroom climate, teacher sensitivity, and regard for student perspectives). Secondly, it includes classroom organization (effective behaviour management, productivity, and instructional learning formats). Thirdly, it contains instructional supports (concept development, quality of feedback, and language modelling). The Framework for Teaching Method for Evaluation Classroom Observation is lastly releasing as 2013 edition. It divides the complex activity of teaching into 22 components clustered into four domains of teaching responsibility. This last tool's edition was conceived to respond to the instructional implications of the American Common Core State Standards. Those standards envision, for literacy and mathematics initially, deep engagement by students with important concepts, skills, and perspectives. They emphasize active, rather than passive, learning by students. In all areas, they place a premium on deep conceptual understanding, thinking and reasoning, and the skill of argumentation. Heather Hill from Harvard University, and Deborah Loewenberg Ball from university of Michigan, had developed “Mathematical Quality of Instruction (MQI)”. Irving Hamer is an education consultant and a former deputy superintendent for academics, technology, and innovation for school system.


Our project's wider objective is to improve teaching and learning effectiveness within K12 classes by exploiting data mining methods for educational knowledge extraction. The researcher realizes three daily visits to mathematics, science, and literacy courses. Using his interactive educational grid, an average of 250 numerical data were stored as quantified teaching and learning practices for one classroom visit for every teacher. At the same time through, and in parallel with on-field activities, distance interactivity via website is processed. At the beginning and for once, each learner from planned classes to be visited fills an individual questionnaire form for learning style identification. He seizes, on another website form, every elementary grade on each question from his maths, science and literacy exams' answer sheets. Those exams statements were previously analysed and saved by the researcher on website. Averages of 150 numerical data were stored as quantified learning performances for every learner. Meetings at partner University for data analytics and educational knowledge extraction were done followed by meetings at inspectorate headquarters for in-depth data. Then, in partner schools, training sessions were the theatres of constructive reflections and feedbacks on major findings about teaching and learning effectiveness. Those actions were reiterated for months. Each year, averagely the performance of 1000 students and the educational practices of 120 teachers will be specifically and tracked. Within summer's months, workshops, seminars, and an international conference will be organised for stakeholders from educational fields. Thus, among project's actions three specific objectives shall be achieved. First, sufficient data on students' profiles and performances related to educational weaknesses and strengths will be provided. Second, teachers' practices inside classrooms at each partner school will be statistically recorded. Third, a complete data mining centre for educational research will be conceived and cognitively interpreted by researchers' teams then findings are exposed for teachers' reflexive thoughts, and discussions within meetings and training sessions

Research methodology and approach


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