Capturing Learner Interaction in ComputerBased Learning Environment: Design and Application Technology Enhanced Learning (TEL) environments help learners learn about conceptually rich domains. These learning environments enhance learnersu0027 skills such as decision making, tradeoff analysis, etc., and is based on pedagogical intervention leveraged by technological affordances. Such environments help learners to solve complex and ill structured problems and provide multiple ways of approaching it. However, novice learneru0027s often find it difficult to take decisions and regulate what they learn in such learning environments. In order to support novice learneru0027s, we need to model their behaviour using their interaction with the system. Hence, logging the learneru0027s interaction with the system is very important. There exists a few systems that are designed and developed to improve thinking skills such as decision making, critical thinking, however they are not designed to log learner behaviour. In this paper we have developed a framework to create data logging mechanism to capture useru0027s interaction with such webbased systems. We have also applied the framework to a webbased learning environment called MEttLE (Modellingbased Estimation Learning Environment) which is a system designed to teach estimation problem solving in the context of electrical engineering. The data logging mechanism successfully captured all the learneru0027s interaction with the system (there were 509 different actions that the user could perform in the system). Additionally, we modelled the learners interaction as a process model using data obtained from 6 learners. The results align with a previous research reported by developers of MEttLE using qualitative data obtained by manually coding screen recordings of learner interaction with the system.

DataDriven Validation of Pedagogical Model  A Case of Blended LCM Model Learning Analytics has been successful in supporting learners and instructors with large and finegrained datasets. However, its impact on practice and pedagogical theory has been limited. One of the solutions to this problem is to apply learning analytics to support evidencebased education. In this paper, we analyse the implementation of Learningcentric MOOCs (LCM), a pedagogical model, which was adopted in a blended classroom setting for an undergraduate Physics course. Extracting learning logs from the elearning platform, we applied Gaussian Mixture model to classify students and built a logistic regression model of the student performance. Our analysis demonstrated that 1) Videos (called LeD in LCM model) and Discussion Forum (called LxI) were positively related to exam scores and 2) studentu0027s access pattern of learning materials also affected exam scores. Our study is an example where learning analytics explores the dynamics of the LCM model for a blendedlearning context and provide evidence from studentsu0027 learning data. We discuss how such analysis is integrated into our technologyenhanced and evidencebased education and learning (TEEL) platform and its implications.