Motivation and Goals
Electronic learning can be quite time consuming and unexciting. Even with some of today’s best computer supported instructional technology, students engage in gaming behaviors associated with less learning. Time spent on task and motivation as key factors of effective learning need to be sustained but contemporary tutoring systems seem to be failing in this respect; all too many students drop out due to low motivation. Can computer tutors build trust and respect with students that would motivate them to learn at all?
This dissertation investigated components of effective learning environments, and proposed an approach to bootstrap a socially intelligent tutoring strategy that operates on top of these components.
We present an approach for computer supported education in the form of a socially intelligent learning environment -- Peoplia (available online). It integrates problem solving and instructional materials into individual and group learning scenarios. A Wizard-of-Oz-driven computer tutor accompanies students to maintain their motivation within the learning environment. The agent can hold off-task conversations and guide students to appropriate learning opportunities. Its tutoring strategy is devised by a reinforcement learning control method that operates on socially motivated state and action spaces induced by the human wizard whose interface facilitates rapid prototyping of relevant states and taking appropriate actions.
To make the learning algorithm feasible, states are grouped into equivalence classes according to wizard selected state features, and contextual and linguistic reflection is employed to adjust the immediate action to the current learner's situation. The proposed approach is pedagogically and technologically robust and is well suited for home study, regular classroom use, as well as for both formative and summative assessment.
The learning environment presented was designed to provide students with socially relevant interventions that would motivate learning. The environment consists of a redesign of typical learning components so that diverse social interaction possibilities are available (to the students). Both problem solving and course note facilities are able to operate in individual (for a single student) or social (for group of students) modes.
The core contributions are:
The approach was evaluated by conducting experiments in the domain of middle school mathematics. To evaluate the robustness of assessments, students worked on tasks generated on-the-fly to discourage cheating, while the human wizard judged the answers. The feasibility study of the socially intelligent agent demonstrated that students who engaged with the agent attained higher learning gains and liked the system more. In a collaborative learning experiment, students solved problems in groups more efficiently when being socially motivated. Finally, the bootstrapping of the socially intelligent tutoring strategy was evaluated in simulated student scenarios. Evaluations suggest that our approach for using computers to support students in the learning process is technologically viable.
In this dissertation, we proposed a learning environment that facilitates student learning using a tutoring agent that adheres to a socially intelligent tutoring strategy. The strategy is modeled on the basis of input from human wizards who guide the bootstrapping process.
The proposed approach puts humans in the loop, and requires non-trivial human participation to sustain long-term operation; for example, student answers need to be judged, and the dialog facility requires human wizards to identify previously not encountered interaction patterns. However, the evaluations suggest that the fruits of this approach may be worth it: