Sergio Gutierrez-Santos

Design requirements, student perception indicators and validation metrics for intelligent exploratory learning environments

The new forms of interaction afforded by innovative technology and open-ended environments provide promising opportunities for exploratory learning. Exploratory environments, however, require appropriate support to lead to meaningful learning outcomes. This paper focuses on the design and validation of intelligent exploratory environments. The goal is twofold: requirements that guide the operationalisation of pedagogical strategies to computer-based support and methodology for the validation of the system. As designers, we need to understand what kind of interaction is conducive to learning and aligned with the theoretical principles behind exploratory learning. We summarise this in the form of three requirements—rare interruption of interaction, co-location of feedback and support towards specific goals. Additionally, developing intelligent systems requires many resources and a long time to build. To facilitate their evaluation, we define three indicators— helpfulness, repetitiveness and comprehension—of students’ perception of the intelligent system and three metrics—relevance, coverage, and scope—which allow the identification of design or implementation problems at various phases of the development. The paper provides a case study with a mathematical microworld that demonstrates how the three requirements are taken into account in the design of the user-facing components of the system and outline the methodology for formative validation of the intelligent support.

Affect Matters: Exploring the Impact of Feedback During Mathematical Tasks in an Exploratory Environment

We describe a Wizard-of-Oz study that investigates the impact of different types of feedback on students’ affective states. Our results indicate the importance of matching carefully the affective state with appropriate feedback in order to help students transition into more positive states. For example when students were confused affect boosts and specific instruction seem to be effective in helping students to be in flow again. We discuss this and other effective ways to and implications for the development of our system and the field in general.

Affective learning: improving engagement and enhancing learning with affect-aware feedback

This paper describes the design and ecologically valid evaluation of a learner model that lies at the heart of an intelligent learning environment called iTalk2Learn. A core objective of the learner model is to adapt formative feedback based on students’ affective states. Types of adaptation include what type of formative feedback should be provided and how it should be presented. Two Bayesian networks trained with data gathered in a series of Wizard-of-Oz studies are used for the adaptation process. This paper reports results from a quasi-experimental evaluation, in authentic classroom settings, which compared a version of iTalk2Learn that adapted feedback based on students’ affective states as they were talking aloud with the system (the affect condition) with one that provided feedback based only on the students’ performance (the non-affect condition). Our results suggest that affect-aware support contributes to reducing boredom and off-task behavior, and may have an effect on learning. We discuss the internal and ecological validity of the study, in light of pedagogical considerations that informed the design of the two conditions. Overall, the results of the study have implications both for the design of educational technology and for classroom approaches to teaching, because they highlight the important role that affect-aware modelling plays in the adaptive delivery of formative feedback to support learning.

A case-based reasoning approach to provide adaptive feedback in microworlds

This paper presents a case-based reasoning (CBR) approach to provide adaptive support in microworlds Interaction in microworlds is complex and unstructured, making the analysis of student behaviour difficult and the provision of computer-based feedback challenging Our approach starts with the ellicitation of expected solutions to microworld tasks (both valid and common mistakes) to generate a case base This is used to evaluate the actions of students and provide adapted feedback.

Case-Based Reasoning Approach to Adaptive Modelling in Exploratory Learning

Exploratory Learning Environments allow learners to use different strategies for solving the same problem. However, not all possible strategies are known in advance to the designer or teacher and, even if they were, considerable time and effort would be required to introduce them in the knowledge base. We have previously proposed a learner modelling mechanism inspired from Case-based Reasoning to diagnose the learners when constructing or exploring models. This mechanism models the learners’ behaviour through simple and composite cases, where a composite case is a sequence of simple cases and is referred to as a strategy. This chapter presents research that enhances the modelling approach with an adaptive mechanism that enriches the knowledge base as new relevant information is encountered. The adaptive mechanism identifies and stores two types of cases: (a) inefficient simple cases, i.e. cases that make the process of generalisation more difficult for the learners, and (b) new valid composite cases or strategies.

Adaptive modelling of users' strategies in exploratory learning using case-based reasoning

In exploratory learning environments, learners can use different strategies to solve a problem. To the designer or teacher, however, not all these strategies are known in advance and, even if they were, introducing them in the knowledge base would involve considerable time and effort. In previous work, we have proposed a case-based knowledge representation, modelling the learners behaviour when constructing/exploring models through simple cases and sequences of cases, called strategies. In this paper, we enhance this approach with adaptive mechanisms for expanding the knowledge base. These mechanisms allow to identify and store inefficient cases, i.e. cases that pose additional difficulty to students in their learning process, and to gradually enrich the knowledge base by detecting and adding new strategies.

Minimal Invasive Integration of Learning Analytics Services in Intelligent Tutoring Systems

A common problem when trying to apply data mining techniques to improve educational systems is the disconnection between those who have the expertise (e.g. Universities) and those who have access to the data (e.g. Small companies). Bringing expertise into educational in-production systems is complicated because companies are reluctant to invest a lot of effort into integrating new technology that they do not fully trust, while the technology cannot prove its worth without access to real, valid data. In this paper we explore the requirements that machine learning systems have to be applied to specific learning problems (sequencing and performance prediction), and then propose a minimally invasive protocol for sequencing (based on web services) to easily integrate Learning Analytics Services into e-learning systems.

Adapting Feedback Types According to Students’ Affective States

Affective states play a significant role in students’ learning behaviour. Positive affective states can enhance learning, while negative ones can inhibit it. This paper describes the development of an affective state reasoner that is able to adapt the feedback type according to students’ affective states in order to evoke positive affective states and as such improve their learning experience. The reasoner relies on a dynamic Bayesian network trained with data gathered in a series of ecologically valid Wizard-of-Oz studies, where the effect of feedback on students’ affective states was investigated.

Similarity-Based Grouping to Support Teachers on Collaborative Activities in an Exploratory Mathematical Microworld

This paper describes a computer-based tool that helps teachers group their students for collaborative activities in the context of secondary school math teaching, the challenge being to organize groups of students based on their recent work, so that their collaboration results in meaningful interactions. The complexity of the exploratory learning tasks is such that the teachers would require too long a time to create meaningful groups without the tool. This paper describes the design of the tool, the algorithms and metrics used for generating the groups, and the pedagogical context in which the tool was designed. This paper reports on the evaluation of the tool. Its recommendations are found to be equivalent to human experts, and the time required is under a few seconds for the usual classroom size. Some possibilities to extend the work to other learning domains, such as programming, are finally considered.

Enhance Teaching and Learning of Computer Programming in Exploratory Learning Environments Using Intelligent Support

Learning programming is an inherently exploratory activity. Supporting this process is not a trivial task and requires resources. This paper proposes the utilisation of an intelligent Exploratory Learning Environment to enhance teaching and learning in this context with minimal cost.