Know about our research

Developing engineering estimation via model-based reasoning

Engineers routinely make estimates of physical quantities such as power before they begin

designing or making. This is known to be challenging for students because they must apply conceptual knowledge to a real-world system, identify the parameters that will dominate the estimate, make assumptions and make judgments regarding numerical values.

Our research showed that experts solve estimation problems following a three-phase model-based reasoning process. Hence, we designed and developed Modelling-based Estimation Learning Environment (MEttLE), an open-ended technology-enhanced learning environment (TELE), that supports students in learning estimation following a phased model-based reasoning process. Broadly, the pedagogy is based on the intertwining of cognitive and metacognitive tasks and triggers learners to build models for solving estimation problems. The major contributions of this project include a detailed characterization of the expert and novice estimation process and its underlying cognitive mechanisms, a set of scaffolds necessary in any learning environment for engineering estimation and a model for solving estimation problems that leads to good estimates.

• Disciplinary Practice: Engineering Estimation

• Topic: Power estimation

• Target audience: Third year and above, Mechanical and Electrical Engineering undergraduates

• No of students trained: 120

• Explore: MEttLE

• Research Team: Aditi Kothiyal, Sahana Murthy

Publications:

1. Kothiyal, A. & Murthy, S. (2018) “MEttLE: A Modelling-based Learning Environment for Undergraduate Engineering Estimation Problem Solving”. In Research and Practice in Technology Enhanced Learning, 13(1), 17.

2. Kothiyal, A. & Murthy, S. (2020). “Disciplinary Model-Based Reasoning and Metacognition Underlies Good Estimation Performance by Engineering Undergraduates ”. In The Interdisciplinarity of the Learning Sciences, 14th International Conference of the Learning Sciences (ICLS) 2020, Volume 2 (pp. 1095-1102). Nashville, Tennessee: International Society of the Learning Sciences.

3. Kothiyal, A. & Murthy, S. (2018). “Exploring How Students Learn Estimation Using a Modelling-based Learning Environment”. In Rethinking Learning in the Digital Age: Making the Learning Sciences Count, Proceedings of the 13th International Conference of the Learning Sciences (ICLS 2018), Vol. 3 (pp. 1543-1545). London: The International Society of the Learning Sciences.

4. Kothiyal, A. & Murthy, S. (2017). “Examining Student Learning of Engineering Estimation from METTLE”. In The 25th International Conference on Computers in Education (pp. 166-175). New Zealand: Asia-Pacific Society for Computers in Education.

5. Kothiyal, A., Murthy, S., & Chandrasekharan, S. (2016). “Hearts Pump and Hearts Beat”: Engineering Estimation as a Form of Model-Based Reasoning. In C. K. Looi, J. Polman, U. Cress, & P. Reimann (Eds.), Transforming Learning and Empowering Learners. Proceedings of the 12th International Conference of the Learning Sciences (ICLS 2016), Vol. 1 (pp. 242-249). Singapore: International Society of the Learning Sciences