Expanding Eye-Tracking Methods to Explain the Socio-Cognitive Effects of Shared Annotations

Social technologies are leading to transformations in education, by empowering the way learners connect to each other, by introducing new means for teaching and learning and by reshaping the way knowledge is delivered. Annotating texts is a learning strategy that is spontaneously adopted by learners during lectures or while reading text books. Students still individually take annotations on paper, that tend to be thrown away when the learning goal is achieved. Students also engage occasionally in the spontaneous practice of note sharing. Our work explores experimentally the benefits of note-taking and note-sharing behaviour. First, we study how sharing student-annotated instructional texts can improve learning, enriching an experimental approach with a new eye tracking method. Second, we conduct experiments on computer-mediated note sharing in the classroom. Our results demonstrate the virtuous circle of note-taking: both annotating while reading and reading annotated documents lead to a better learning achievement. In the first experimental study, we measure if the presence of annotations, on an instruc- tional text, can influence the reading pattern, and how visual features of annotations can elicit the reader’s attention. To complement the results concerning learning and reading comprehension, we look into the readers’ gaze patterns, to explain how the students’ learning outcome relates to reading annotated texts. For this purpose, we design a novel eye tracker, that can be used to study reading and note taking in unconstrained experimental settings. This eye tracking system is based on a systematic error correction procedure that exploits the appearance similarity between the annotated texts and the spatial distribution of the fixation points. We show that this method can extract accurate gaze measures without introducing experimental constraints, that could disturb the note taking process and affect the readers’ comprehension. In the second study, we move from a controlled experimental setting to the classroom. We discuss how the use of technology can facilitate a spontaneous transition from personal to shared annotations and support students in the learning process. We complement the analysis by reporting a friendship bias in browsing the shared annotated material. We further speculate on the potential of shared annotations in triggering adaptations of the instructional material and teaching workflow. These two studies provided an insightful understanding of the effects of student-generated annotations on reading comprehension, and the underlying impact on a community of learners. The obtained findings should inspire further experimentation on social learning environments, meant to facilitate knowledge sharing through shared annotations and their diffusion within educational institutions.

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

Design and evaluation of digital tools to expand experience in vocational education

Kevin Gonyop Kim

Vocational education and training (VET) is the most popular form of upper-secondary education in Switzerland, with more than two-thirds of the teenagers enrolling in a VET program after finishing their compulsory education. The vast majority of the Swiss VET systems have a dual-track structure where students learn in schools for one or two days per week while they do an apprenticeship in a company for the remaining days. Their professional competencies are developed through a combination of workplace experiences reinforced by the theoretical knowledge learned in school.While the apprenticeship at workplaces provides valuable experience to the learners, one concern is that there is a lack of diversity in the experiences of an individual apprentice. Motivated by this limited experience of VET learners in the workplace, the goal of our research has been to explore the possibilities of expanding their experiences. Focusing on design-related professions in VET, we consider exploring digital variations of a design as a way to expand the original experience related to the design. The research objectives of this thesis are (1) exploring possibilities to expand the experience of learners in design-related VET and (2) investigating the potential benefits of the expanded experience. To accomplish the research objectives, we formalize the idea of expanding experience for VET learners by defining the three dimensions of expansion: parametric, temporal, and social. We explore these three dimensions in three studies.For the parametric dimension, we investigate how learners explore a multidimensional space of possible designs using a digital tool and how we can support the process with an interface that enables disentangled exploration of design parameters. We present a tool called BloomGraph that has been developed for florist apprentices to explore flower bouquet designs, and the results from an experimental study that investigates the effectiveness of a graph interface for design exploration.For the temporal dimension, we explore how we can support the VET learners with envisioning the designs that change over time. We present a virtual-reality tool called GardenVR developed for gardener apprentices that allows creating a garden and exploring it in an immersive environment. In an experimental study with gardener apprentices, we investigate how they explore the time dimension using the tool, compare the tool with the current method of practice, paper-sketching, and study how the two interfaces can be combined in an instructional design to enhance the design outcome.Lastly, for the social dimension, we present a tool called Mixplorer that has been designed for a classroom scenario of gardener apprentices. The tool provides a way to socially explore design spaces by allowing the apprentices to create an initial design and recombine it with the designs of other learners. We present the results of two studies conducted using the tool that investigate its feasibility in the VET setting, potential benefits for the learners, and the effectiveness of the design-mixing process in creative practices.

EPFL2022

Attentional Processes in Natural Reading: the Effect of Margin Annotations on Reading Behaviour and Comprehension

Pierre Dillenbourg, Frédéric Kaplan, Tabea Koll, Andrea Mazzei

We present an eye tracking study to investigate how natural reading behavior and reading comprehension are influenced by in-context annotations. In a lab experiment, three groups of participants were asked to read a text and answer comprehension questions: a control group without taking annotations, a second group reading and taking annotations, and a third group reading a peer-annotated version of the same text. A self-made head-mounted eye tracking system was specifically designed for this experiment, in order to study how learners read and quickly re-read annotated paper texts, in low constrained experimental conditions. In the analysis, we measured the phenomenon of annotation-induced overt attention shifts in reading, and found that: (1) the reader's attention shifts toward a margin annotation more often when the annotation lies in the early peripheral vision, and (2) the number of attention shifts, between two different types of information units, is positively related to comprehension performance in quick re-reading. These results can be translated into potential criteria for knowledge assessment systems.