Doctoral Project: Educationally effective intervention methods for Intelligent Tutoring Systems incorporating cognitive, motivational and emotional components

Part of research project: From understanding learners’ adaptive motivation and emotion to designing social learning companions

Principal Investigators

Description of the research project

We aim to develop an approach for integrated game- and agent-based Intelligent Tutoring Systems (ITS) and computational models that help to optimize scaffolding in social learning situations, and even go one step further and create a robotic learning companion based on these results. Researchers from analytic (educational research: Rebecca Lazarides) and synthetic disciplines (adaptive systems / robotics: Verena Hafner, educational technology / computer science: Niels Pinkwart) collaborate on this project. Collaborations with other projects that focus on social learning are intended.

The three objectives of our project are:

i) Examine how novel user modeling approaches and feedback strategies in ITSs incorporating virtual agents can enhance positive emotions and motivation (self-regulation, goal orientations) and reduce negative emotions in social learning situations and can thereby be used to impede inequalities in education.

ii) Explore the (moderating and mediating) processes that underlie the relations between pedagogical agents’ ‘behaviors’ and learners’ performance by investigating psychological factors that strengthen or reduce the effects of ITS on learners’ motivation and emotion.

iii) Create a robotic learning companion that keeps an updated model/simulation of the learner and their current knowledge, motivational and emotional state and acts accordingly.

Description of the doctoral project

This PhD project focuses on researching effective intervention methods for Intelligent Tutoring Systems incorporating cognitive, motivational and emotional components. The research will build on existing ITS platforms and research, including prior work of the supervisor. The PhD work involves the design of ITS components that are able to predict the level of knowledge acquisition and performance of a learner with a specific level of emotion and motivation. These models also need to contain the level of uncertainty (important for the necessarily heuristic estimation of emotion and motivation). Based on this, an educational intervention model (e.g., which tasks and materials should be used for different groups of learners, which other feedback is appropriate) will be designed – first, based on data of learners that participated in educational large-scale data assessments that include competence measures but also motivational measures (i.e., PISA), and general literature on feedback provision in ITSs in the context of uncertainty. In an iterative fashion, the intervention models will be refined as learners use different versions of the developed ITS in pilot studies. As a larger-scale evaluation study, the PhD work involves an experiment in which two versions of the ITS are tested with groups of students in experimental settings. Here, one goal is to compare the level of knowledge acquisition, performance, motivation and emotion on given tasks among different groups of learners. Another goal will be to compare different implementation variants of the ITS, involving both “classical” human-machine interfaces as well as implementations that employ humanoid robots (“Pepper”).


Project start date: October 1, 2019


Applicants must hold a Diploma/Master’s degree in Computer Science or related sciences. They should be interested in interdisciplinary collaboration, performing empirical studies of learning with technology and performing human-robot-interaction experiments. Applicants should have proven skills/background in following topics:

  • Educational technology, ideally educational data mining or intelligent tutoring systems
  • Programming skills


The following skills are desirable:

  • Computational modelling / machine learning / developmental robotics
  • Interest in performing human-robot-interaction experiments


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