AngeLA: Putting the Teacher in Control of Student Privacy in the Online Classroom

Learning analytics (LA) is often considered as a means to improve learning and learning environments by measuring student behaviour, analysing the tracked data and acting upon the results. The use of LA tools implies recording and processing of student activities conducted on software platforms. This paper proposes a flexible, contextual and intuitive way to provide the teacher with full control over student activity tracking in online learning environments. We call this approach AngeLA, inspired by an angel guarding over LA privacy. AngeLA mimics in a virtual space the privacy control mechanism that works well in a physical room: if a person is present in a room, she is able to observe all activities happening in the room. AngeLA serves two main purposes: (1) it increases the awareness of teachers about the activity tracking and (2) provides an intuitive way to manage the activity tracking permissions. This approach can be applied to various learning environments and social media platforms. We have implemented AngeLA in Graasp, a social platform that fosters collaborative activities.

Learning Analytics for Adaptive and Self-Improving Learning Environments for Inductive Teaching

Louis Pierre Faucon

The emergence of digital technology is changing education in many ways. A particularly interesting aspect of this transformation is the development of learning environments that can automatically adapt to individual students and can collect data in order to automatically improve themselves. How a learning environment should adapt to students is dependent on the pedagogical approach. In this thesis, we contribute to adaptive and self-improving learning environments that support students during inductive reasoning activities. Inductive teaching is a pedagogical approach that yields very beneficial outcomes for students, but has not received sufficient technological support. Using inductive reasoning, students infer general rules or concepts from observations. This pedagogical approach is motivated by the idea that learning outcomes will increase when students construct the knowledge by themselves.First, we contribute to student modeling and self-improvement for learning environments. We share the results of a study that we conducted using data collected in several large classrooms. We develop a mechanism that collects data, estimates the progress of students and predicts in real-time their future progress. The goal of our approach is to aggregate the predictions to support adaptive decisions by teachers in classrooms. Moreover, we share the results of a second study with MOOC students. We use a generative model, based on a Semi-Markov Chain, to model and simulate sequences of actions taken by students on the MOOC platform. Additionally, based on observations of students from arbitrary Bayesian models, we propose and analyse a self-improving algorithm relying on Thompson Sampling optimisation that maximizes students learning outcomes over time.Second, we contribute to the analysis of inductive teaching and individual differences in inductive reasoning. We report results on an experiment with 222 students solving tasks of categorisation of images. Our study revealed that students individually differ in how they choose to classify examples based on feature differences between the examples. We define the individual differences of students as their inductive bias. This result motivates the importance of adaptivity in the selection of examples during inductive learning activities. Additionally, we analyse how much students change their inductive bias when confronted to negative feedback. We find that students are influenced by the feedback, but that this influence decays after a short period of time.Third, we contribute multiple algorithms that constitute together the necessary components of an adaptive and self-improving learning environment for inductive teaching. Notably, we designed algorithms for a learning environment to extract representations of students' biases from data, estimate and trace individual students' biases, and to optimally select personalised examples for an inductive learning activity.Finally, we conclude by describing the concept of probabilistic testing as a promising assessment mechanism for inductive learning environments. We provide preliminary observations and theoretical results for the use of probabilistic testing in the context of adaptive inductive teaching. In particular, by using probabilistic tests, a learning environment can estimate more quickly students' inductive biases.

EPFL2021

Robot Analytics: What Do Human-Robot Interaction Traces Tell Us About Learning?

Wafa Monia Benkaouar Johal, Pierre Dillenbourg, Jauwairia Nasir, Utku Norman, Jennifer Kaitlyn Olsen, Sina Shahmoradi

In this paper, we propose that the data generated by educational robots can be better used by applying learning analytics methods and techniques which can lead to a deeper understanding of the learners’ apprehension and behavior as well as refined guidelines for roboticists and improved interventions by the teachers. As a step towards this, we put forward analyzing behavior and task performance at team and/or individual levels by coupling robot data with the data from conventional methods of assessment through quizzes. Classifying learners/teams in the behavioral feature space with respect to the task performance gives insight into the behavior patterns relevant for high performance, which could be backed by feature ranking. As a use case, we present an open-ended learning activity using tangible haptic-enabled Cellulo robots in a classroom-level setting. The pilot study, spanning over approximately an hour, is conducted with 25 children in teams of two that are aged between 11-12. A linear separation is observed between the high and low performing teams where two of the behavioral features, namely number of distinct attempts and the visits to the destination, are found to be important. Although the pilot study in its current form has limitations, e.g. its low sample size, it contributes to highlighting the potential of the use of learning analytics in educational robotics. © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

IEEE2019

Learning Analytics for Adaptive and Self-Improving Learning Environments for Inductive Teaching

Louis Pierre Faucon

EPFL2021