Using the Flipped Classroom to Improve Knowledge Creation of Master's-Level Students in Engineering

Using the Flipped Classroom to Improve Knowledge Creation of Master's-Level Students in Engineering

Sachin Ahuja
DOI: 10.4018/978-1-5225-2399-4.ch028
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Student engagement in traditional teacher centered model of teaching is limited to independent working or working in a small group on a task designed by the teacher. Flipped classroom is a blended learning strategy that reverses the traditional educational arrangement by delivering instructional content, often online, outside of the classroom and moves activities, including those that may have traditionally been considered homework, into the classroom. Various studies support and recommend flipped model of teaching at graduate and undergraduate level but very less have analyzed the impact of flipped classroom on academic performance and especially knowledge creation at post graduate level. In this paper we are analyzing the performance and knowledge creation of master's level students using Data Mining Techniques in a flipped classroom model.
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Numerous studies have been conducted on various facets of Flipped Classrooms focusing on the increased levels of active learning, student’s participation and collaboration among the students in the flipped class and effect on coping with absence from the class. None of the studies has compared the level of knowledge creation in flipped classroom. This study uses educational data mining methods and quasi experimental methods to compare the academic performance of the students at master’s level and secondly the comparison of knowledge creation in flipped classroom with normal teaching setting.

Key Terms in this Chapter

Knowledge Creation: Knowledge creation is transfer combination or conversion of different types of knowledge as users practice, interact, and learn. Knowledge creation is a product of the interplay between knowing and knowledge. Knowledge creation can only be achieved after thorough understanding of underlying concepts and application of those concepts to contribute towards a larger knowledge pool in terms of understanding, creating or converting through practice action and interaction with increased input of creativity and innovation.

Opinion Mining: Opinion mining also known as Sentiment analysis refers to the use of natural language processing, text analysis and computational linguistics to identify and extract subjective information in source materials. Sentiment analysis is widely applied to reviews and social media for a variety of applications, ranging from marketing to customer service. The aim of sentiment analysis is to determine the attitude of a speaker or a writer with respect to some topic or the overall contextual polarity of a document. The attitude may be his or her judgment or evaluation, affective state i.e. the emotional state of the author when writing, or the intended emotional communication i.e. the emotional effect the author wishes to have on the reader.

Flipped Learning: Flipped Learning is a pedagogical approach in which direct instruction moves from the group learning space to the individual learning space, and the resulting group space is transformed into a dynamic, interactive learning environment where the educator guides students as they apply concepts and engage creatively in the subject matter. The role of the teacher in the class changes from “Sage on the stage” to Guide on the side”. In flipped learning strategy the traditional educational arrangement is reversed in terms of delivery of instructional content, often online, outside of the classroom and moves activities, including those that may have traditionally been considered homework, into the classroom.

Educational Data Mining: Educational Data Mining is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from educational settings, and using those methods to better understand students, and the settings which they learn in. Whether educational data is taken from students' use of interactive learning environments, computer-supported collaborative learning, or administrative data from schools and universities, it often has multiple levels of meaningful hierarchy, which often need to be determined by properties in the data itself, rather than in advance. Issues of time, sequence, and context also play important roles in the study of educational data.

Chitkara University: Chitkara University is a leading non-profit private university in India with its campus in Punjab and Himachal Pradesh, India. Chitkara University is a UGC recognized university with the right to confer degrees as per the sections 2(f) and 22(1) of the UGC Act, 1956. The university offers full courses in undergraduate and postgraduate degree programs in the fields of Engineering, Information Technology, Management, Hospitality, Hotel Administration Architecture, Pharmacy, Mass Communication & Journalism, Architecture, Pharmacy, and Teacher Training.

Data Mining: Data Mining refers to extracting or mining knowledge from large amounts of data. Data mining is a multidisciplinary field, drawing work from areas including database technology, artificial intelligence, machine learning, neural networks, statistics, pattern recognition, knowledge based systems, knowledge acquisition, information retrieval, high performance computing, and data visualization. Data mining is used to uncover hidden patterns in the underlying data which can be used for decision making process.

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