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Course Detail

Course Name Learning Engineering
Course Code 24CLT663
Program M. Sc. Cognitive Sciences, Learning and Technology
Semester Elective
Credits 3
Campus Amritapuri


Unit I

Unit I – Introduction to Learning Engineering and its Impact on Lifelong Learning
Interdisciplinarity in LE and a model of self-initiated education
Scenario of developed countries and the Indian context under NEP2020

Unit II

Unit II – Evidence-based Decision-making within Educational Technology
Evidence ecosystem and evidence-based thinking under the lens of Scientific thinking History: A marriage among educational science, psychology, and computer science EdTech industry and tools

Unit III

Unit III – (Multimodal) Learning Analytics and Technology-Enhanced Learning
Technology-Enhanced Learning under Educational Technology and European Approach
Major milestone research projects, Learning performance and context, Tools, and Challenges and future lines
Soft and hard MMLA, Modalities and tools, and Learning constructs, and data processing pipeline

Unit IV

Unit IV – Common Research Methodologies
Practitioner involvement and the context of practice Iterative nature
Design-Based Research, Participatory Approach, and Engineering Method

Unit V

Unit V – Adoption and Milestones of LE
Adaptability, Scalability while meeting reusability and Challenges Assessment tools and Dashboards
LE tools competition

Course Objectives and Outcomes

Prerequisite: A foundational understanding of educational psychology, computer science principles, and basic research methodologies.

Course Objectives:

  1. Investigate current directions in Learning Engineering, including its history, interdisciplinarity, research projects, milestones, approaches, tools, research designs, career opportunities, and philosophical stances.
  2. Identify trends in Learning Engineering to understand its evolving nature and potential future developments.
  3. Explore core and applied sciences at different levels of Learning Engineering to grasp the underlying principles and methodologies.
  4. Cluster existing Learning Engineering tools based on parameters such as technological choice, pedagogical underpinning, and educational impact to understand their diverse applications and implications.
  5. Plan an individual Learning Engineering tool proposal addressing a specific educational problem, fostering innovation, critical thinking, and problem-solving skills.

Course Outcomes:

  • CO1: Gain a comprehensive understanding of Learning Engineering, its historical evolution, interdisciplinary nature, and diverse educational applications.
  • CO2: Analyze and evaluate current trends and advancements in Learning Engineering, enabling informed decision-making and adaptation to emerging technologies and methodologies.
  • CO3: Demonstrate proficiency in identifying and applying core scientific principles and methodologies relevant to Learning Engineering, facilitating the development of research and analytical skills.
  • CO4: Evaluate and categorize existing Learning Engineering tools based on various criteria, fostering the ability to assess technological solutions and their potential impact on educational practices.
  • CO5: Develop and present a viable Learning Engineering tool proposal addressing a
    real-world educational challenge, showcasing the ability to conceptualize, plan, and communicate innovative solutions in the field.


  • Ability to critically analyze and evaluate educational technology tools and methodologies within Learning Engineering.
  • Proficiency in designing and conducting research projects in Learning Engineering, including data collection, analysis, and interpretation.
  • Competence in applying evidence-based decision-making strategies to address complex educational challenges using Learning Engineering principles and tools.
  • Skill in synthesizing interdisciplinary knowledge from educational science, psychology, and computer science to inform Learning Engineering practices.
  • Capability to conceptualize, develop, and propose innovative Learning Engineering solutions tailored to specific educational contexts and needs.

Course Outcomes (CO) – Program Outcomes (PO) Mappings


Textbooks and Papers

  1. Dym, C. L. (1999). Learning engineering: Design, languages, and experiences. Journal of Engineering Education, 88(2), 145-148.
  2. Johri, A., & Olds, B. M. (2011). Situated engineering learning: Bridging engineering education research and the learning sciences. Journal of Engineering Education, 100(1), 151-185.
  3. Smith, M. K., & Ferrier, F. (2001). Lifelong learning. The encyclopedia of informal education.
  4. Clancy, C. M., & Cronin, K. (2005). Evidence-based decision making: global evidence, local decisions. Health affairs, 24(1), 151-162.
  5. Honig, M. I., & Coburn, C. (2008). Evidence-based decision making in school district central offices:Toward a policy and research agenda. Educational policy, 22(4), 578-608.
  6. Miller, S. A., & Forrest, J. L. (2001). Enhancing your practice through evidence-based decision making: PICO, learning how to ask good questions. Journal of Evidence Based Dental Practice, 1(2), 136-141.
  7. Ely, D. P. (1990). Conditions that facilitate the implementation of educational technology innovations. Journal of research on computing in education, 23(2), 298-305.
  8. Kirkwood, A., & Price, L. (2014). Technology-enhanced learning and teaching in higher education: what is ‘enhanced’and how do we know? A critical literature review. Learning, media and technology, 39(1), 6-36.
  9. Manca, S., & Ranieri, M. (2013). Is it a tool suitable for learning? A critical review of the literature on F acebook as a technology‐enhanced learning environment. Journal of Computer Assisted Learning, 29(6), 487-504.
  10. Bayne, S. (2015). What’s the matter with ‘technology-enhanced learning’?. Learning, media and technology, 40(1), 5-20.
  11. Ferguson, R. (2012). Learning analytics: drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5/6), 304-317.
  12. Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 1380-1400.
  13. Greller, W., & Drachsler, H. (2012). Translating learning into numbers: A generic framework for learning analytics. Journal of Educational Technology & Society, 15(3), 42-57.
  14. Blikstein, P. (2013, April). Multimodal learning analytics. In Proceedings of the third international conference on learning analytics and knowledge (pp. 102-106).
  15. Blikstein, P., & Worsley, M. (2016). Multimodal learning analytics and education data mining: Using computational technologies to measure complex learning tasks. Journal of Learning Analytics, 3(2), 220-238.
  16. Shankar, S. K., Prieto, L. P., Rodríguez-Triana, M. J., & Ruiz-Calleja, A. (2018, July). A review of multimodal learning analytics architectures. In 2018 IEEE 18th international conference on advanced learning technologies (ICALT) (pp. 212-214). IEEE.
  17. Anderson, T., & Shattuck, J. (2012). Design-based research: A decade of progress in education research?. Educational researcher, 41(1), 16-25.
  18. Lin, C. C., & Tsai, C. C. (2009). The relationships between students’ conceptions of learning engineering and their preferences for classroom and laboratory learning environments. Journal of Engineering Education, 98(2), 193-204.
  19. Sheppard, S., Gilmartin, S., Chen, H. L., Donaldson, K., Lichtenstein, G., Eris, O., … & Toye, G. (2010). Exploring the Engineering Student Experience: Findings from the Academic Pathways of People Learning Engineering Survey (APPLES). TR-10-01. Center for the Advancement of Engineering Education (NJ1).

Reference Books

  • Dym, C. L., Agogino, A. M., Eris, O., Frey, D. D., & Leifer, L. J. (2005). Engineering design thinking, teaching, and learning. Journal of engineering education, 94(1), 103-120.
  • Aspin, D. N., & Chapman, J. D. (2000). Lifelong learning: concepts and conceptions. International Journal of lifelong education, 19(1), 2-19.
  • Laal, M., & Salamati, P. (2012). Lifelong learning; why do we need it?. Procedia-Social and Behavioral Sciences, 31, 399-403.
  • Spector, J. M. (2001). An overview of progress and problems in educational technology. Interactive educational multimedia: IEM, 27-37.
  • Lazar, S. (2015). The importance of educational technology in teaching. International Journal of Cognitive Research in Science, Engineering and Education, 3(1), 111-114.
  • Mor, Y., & Winters, N. (2007). Design approaches in technology-enhanced learning. Interactive Learning Environments, 15(1), 61-75.
  • Wang, F., & Hannafin, M. J. (2005). Design-based research and technology-enhanced learning environments. Educational technology research and development, 53(4), 5-23.

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