Sale!

Data Mining and Learning Analytics Applications in Educational Research 1st Edition by Samira ElAtia, Donald Ipperciel, Osmar Zaïane 1118998235 ‎ 9781118998236

Original price was: $70.00.Current price is: $24.99.

Instant download Data Mining and Learning Analytics Applications in Educational Research 1118998235 after payment

SKU: EB_107616 Category:

Data Mining and Learning Analytics Applications in Educational Research 1st Edition by Samira ElAtia, Donald Ipperciel, Osmar Zaïane – Ebook PDF Instant Download/Delivery:  1118998235, ‎9781118998236
Full download Data Mining and Learning Analytics Applications in Educational Research 1st Edition after payment


Product details:

ISBN 10:  1118998235 ‎  
ISBN 13:  9781118998236
Author: Samira ElAtia, Donald Ipperciel, Osmar R. Zaïane

Addresses the impacts of data mining on education and reviews applications in educational research teaching, and learning 

This book discusses the insights, challenges, issues, expectations, and practical implementation of data mining (DM) within educational mandates. Initial series of chapters offer a general overview of DM, Learning Analytics (LA), and data collection models in the context of educational research, while also defining and discussing data mining’s four guiding principles― prediction, clustering, rule association, and outlier detection. The next series of chapters showcase the pedagogical applications of Educational Data Mining (EDM) and feature case studies drawn from Business, Humanities, Health Sciences, Linguistics, and Physical Sciences education that serve to highlight the successes and some of the limitations of data mining research applications in educational settings. The remaining chapters focus exclusively on EDM’s emerging role in helping to advance educational research―from identifying at-risk students and closing socioeconomic gaps in achievement to aiding in teacher evaluation and facilitating peer conferencing. This book features contributions from international experts in a variety of fields.

  •  Includes case studies where data mining techniques have been effectively applied to advance teaching and learning
  • Addresses applications of data mining in educational research, including: social networking and education; policy and legislation in the classroom; and identification of at-risk students
  • Explores Massive Open Online Courses (MOOCs) to study the effectiveness of online networks in promoting learning and understanding the communication patterns among users and students
  • Features supplementary resources including a primer on foundational aspects of educational mining and learning analytics

Data Mining and Learning Analytics: Applications in Educational Research is written for both scientists in EDM and educators interested in using and integrating DM and LA to improve education and advance educational research.

 

Data Mining and Learning Analytics Applications in Educational Research 1st Table of contents:

Part I: AT THE INTERSECTION OF TWO FIELDS from Data Mining and Learning Analytics Applications in Educational Research:

Chapter 1: Educational Process Mining: A Tutorial and Case Study Using Moodle Data Sets

  • Authors: Cristóbal Romero, Rebeca Cerezo, Alejandro Bogarín, Miguel Sánchez-Santillán
  • This chapter introduces educational process mining (EPM), focusing on how data mining techniques can be applied to learning management system (LMS) data (specifically from Moodle). The authors provide a tutorial on the process and illustrate its application through case studies using Moodle datasets to analyze student behavior and interactions in an educational setting.

Chapter 2: On Big Data and Text Mining in the Humanities

  • Authors: Geoffrey Rockwell, Bettina Berendt
  • This chapter addresses the application of text mining and big data techniques to the humanities. The authors discuss the challenges and opportunities of using computational methods to analyze large volumes of textual data, especially in the context of educational research and the humanities.

Chapter 3: Finding Predictors in Higher Education

  • Authors: David Eubanks, William Evers Jr., Nancy Smith
  • This chapter explores predictive analytics in higher education. It looks at how various factors such as demographic data, academic performance, and engagement can be used to predict student success and dropout rates. The authors highlight how data mining can help universities identify students at risk and improve retention strategies.

Chapter 4: Educational Data Mining: A MOOC Experience

  • Authors: Ryan S. Baker, Yuan Wang, Luc Paquette, Vincent Aleven, Octav Popescu, Jonathan Sewall, Carolyn Rosé, Gaurav Singh Tomar, Oliver Ferschke, Jing Zhang, Michael J. Cennamo, Stephanie Ogden, Therese Condit, José Diaz, Scott Crossley, Danielle S. McNamara, Denise K. Comer, Collin F. Lynch, Rebecca Brown, Tiffany Barnes, Yoav Bergner
  • This chapter examines the application of educational data mining (EDM) in Massive Open Online Courses (MOOCs). The authors explore the challenges of analyzing data from online learning environments, discussing how learning analytics can be used to improve the design and delivery of MOOCs by tracking learner behaviors and predicting performance.

Chapter 5: Data Mining and Action Research

  • Authors: Ellina Chernobilsky, Edith Ries, Joanne Jasmine
  • This chapter discusses the integration of data mining techniques with action research methodologies. The authors provide examples of how data mining can enhance action research in educational settings, facilitating the identification of patterns and insights that inform the improvement of teaching and learning practices.

Chapter 6: Design of an Adaptive Learning System and Educational Data Mining

  • Authors: Zhiyong Liu, Nick Cercone
  • This chapter discusses the design of adaptive learning systems, which personalize the educational experience based on students’ learning needs and progress. It highlights how educational data mining (EDM) can be integrated into adaptive learning platforms to analyze student interactions and performance, enabling the system to make real-time adjustments to instructional content. The authors also explore various data mining algorithms and their applications in adaptive learning systems.

Chapter 7: The “Geometry” of Naïve Bayes: Teaching Probabilities by “Drawing” Them

  • Author: Giorgio Maria Di Nunzio
  • In this chapter, the author explores how the Naïve Bayes classifier, a popular data mining algorithm, can be used to teach probabilities. The chapter introduces a novel pedagogical approach by visually representing the algorithm and its concepts, making it easier for students to grasp the theoretical aspects of probability and statistics. This approach emphasizes how mathematical concepts can be taught through data mining tools and visualizations.

Chapter 8: Examining the Learning Networks of a MOOC

  • Authors: Meaghan Brugha, Jean-Paul Restoule
  • This chapter investigates the learning networks in MOOCs (Massive Open Online Courses), focusing on how learners interact and collaborate in large online environments. The authors apply social network analysis (SNA) techniques to explore patterns of student engagement, communication, and collaboration, shedding light on how these factors influence learning outcomes in MOOCs.

Chapter 9: Exploring the Usefulness of Adaptive eLearning Laboratory Environments in Teaching Medical Science

  • Authors: Thuan Thai, Patsie Polly
  • This chapter focuses on the application of adaptive eLearning laboratory environments in medical education. The authors discuss how such systems, combined with educational data mining techniques, can provide personalized learning experiences for medical students. They highlight the effectiveness of adaptive learning in promoting deeper learning, improving clinical decision-making skills, and enhancing student engagement in medical science education.

Chapter 10: Investigating Co-Occurrence Patterns of Learners’ Grammatical Errors Across Proficiency Levels and Essay Topics Based on Association Analysis

  • Author: Yutaka Ishii
  • This chapter uses data mining techniques to explore patterns in learners’ grammatical errors, with a focus on how these errors vary across different proficiency levels and essay topics. The author employs association rule mining, a method for discovering relationships between variables in large datasets, to identify frequent co-occurrence patterns of grammatical errors. These insights are then used to inform language instruction and curriculum development.

Part II: PEDAGOGICAL APPLICATIONS OF EDM

This section of the book shifts from the technical and methodological aspects of EDM to explore its applications in pedagogy and teaching strategies. The chapters in Part II examine how EDM can be leveraged to enhance personalized learning, improve instructional design, and optimize educational outcomes through adaptive learning and targeted interventions.

Chapter 11: Mining Learning Sequences in MOOCs: Does Course Design Constrain Students’ Behaviors or Do Students Shape Their Own Learning?

  • Authors: Lorenzo Vigentini, Simon McIntyre, Negin Mirriahi, Dennis Alonzo
  • This chapter investigates how students’ learning behaviors in MOOCs can be influenced by course design, and whether learners shape their own learning paths or are constrained by the structure of the course. The authors employ sequence mining techniques to analyze students’ navigation patterns through course materials, seeking to understand how learners engage with the content and whether certain design features encourage or hinder active learning.

Chapter 12: Understanding Communication Patterns in MOOCs: Combining Data Mining and Qualitative Methods

  • Authors: Rebecca Eynon, Isis Hjorth, Taha Yasseri, Nabeel Gillani
  • This chapter explores the use of data mining to examine communication patterns in MOOCs. It highlights the benefits of combining quantitative data analysis with qualitative methods to gain deeper insights into how students interact, collaborate, and communicate in online learning environments. The authors use both text mining and social network analysis to investigate discussion forums, collaborative projects, and peer interactions in MOOCs.

Chapter 13: An Example of Data Mining: Exploring the Relationship Between Applicant Attributes and Academic Measures of Success in a Pharmacy Program

  • Authors: Dion Brocks, Ken Cor
  • This chapter provides a case study of using data mining to explore the relationship between applicants’ personal attributes (such as academic background and socio-economic status) and their academic success in a pharmacy program. The authors use predictive modeling to identify factors that contribute to success in the program, with the goal of improving the selection process and better predicting student outcomes.

Chapter 14: A New Way of Seeing: Using a Data Mining Approach to Understand Children’s Views of Diversity and “Difference” in Picture Books

  • Authors: Robin A. Moeller, Hsin-liang Chen
  • In this chapter, the authors apply data mining techniques to analyze children’s perceptions of diversity and “difference” in picture books. By mining text and visual data from a large corpus of children’s literature, they explore how books represent diversity and how these representations might shape children’s views and understanding of social issues. This analysis also has implications for curriculum development and promoting inclusive education.

Chapter 15: Data Mining with Natural Language Processing and Corpus Linguistics: Unlocking Access to School Children’s Language in Diverse Contexts to Improve Instructional and Assessment Practices

  • Authors: Alison L. Bailey, Anne Blackstock-Bernstein, Eve Ryan, Despina Pitsoulakis
  • This chapter delves into the use of data mining combined with natural language processing (NLP) and corpus linguistics to analyze school children’s language across various educational contexts. The authors explain how these techniques can be used to better understand student language development and improve instructional practices, particularly in terms of assessment and feedback. The chapter demonstrates how analyzing language data can inform both formative and summative assessment practices, providing teachers with more personalized and precise insights into student progress.

People also search for Data Mining and Learning Analytics Applications in Educational Research 1st:

what is data mining and analytics
    
applications of data analytics
    
applications of data mining
    
data mining and ai
    
data mining and predictive analytics larose

Reviews

There are no reviews yet.

Be the first to review “Data Mining and Learning Analytics Applications in Educational Research 1st Edition by Samira ElAtia, Donald Ipperciel, Osmar Zaïane 1118998235 ‎ 9781118998236”