Projects

Phase 2 Projects

Learning dynamic feedback in intelligent tutoring systems (DynaFIT)


Project leaders: Barbara Hammer (Bielefeld), Nils Pinkwart (Berlin)

Researchers: Benjamin Paaßen (Bielefeld), Sebastian Groß (Berlin)

Administration: Giesela Weitekemper (Bielefeld), Eva Sandig (Berlin)

Associates: Haibo He (Rhode Island)

Summary:

The rapidly increasing availability of online educational resources and the recent attention gained by distance education methods such as massive open online courses (MOOCs) show the importance of intelligent tutoring systems (ITS) as adaptive educational technologies that can personalize e-Learning. Classical ITSs require an exact  formalization of the learning task and learner-system interactions. Hence their applicability is typically limited to well-defined domains. In addition, their labor-intensive preparation
restricts their use to static, large-scale applications where development costs do not play a significant role. Within the first period of the FIT project, we have developed a FIT ITS infrastructure which allows the construction of ITSs in ill-defined domains based on machine learning techniques. In particular, we have developed prototype-based machine learning models for structures and structure-metric adaptation techniques which enable an autonomous organization of an ITS solution space in ill-defined domains, based on which feedback provision strategies can be grounded.
So far, the developed machine learning techniques are restricted to single tasks, and feedback provision is not tailored to individual users and their progress. The goal of DynaFIT is to (i) develop machine learning models which can generalize across different tasks and user behaviors and, based thereon, (ii) to enhance FIT ITSs via dynamic user-adaptive feedback and open learner models in ill-defined domains.
More specifically, we will develop cross-task dimensionality reduction (DR) techniques for structures which generate a task-independent representation of different solution spaces in one common latent space. This enables autonomous information transfer across tasks and a generalization from possibly singular user behavior to relevant underlying principles. This representation constitutes a key prerequisite for obtaining the following central components of DynaFIT: a visualization of relevant characteristics of solution spaces and learner behaviour (open learner models); a representation of user behaviour across tasks as low dimensional time series, for which classical data analysis techniques as well as relevance learning as developed in the first period of the FIT project are available; finally, based thereon, dynamic feedback provision strategies adjusted to this time series data. In this realm, we will exemplarily investigate dynamic peering strategies (highly relevant for larger online courses) in detail.
In addressing information transfer by cross-task dimensionality reduction, DynaFIT  contributes to a central topic of the SPP: the autonomous development of suitable representations for learning. Further, the envisioned enrichment of ITSs in ill-defined domains by dynamic feedback provision and open learner models bears great potential for highly dynamic large-scale educational technology facilities such as MOOCs.

Phase 1 Projects

Learning dynamic feedback in intelligent tutoring systems (DynaFIT)


Project leaders: Barbara Hammer (Bielefeld), Nils Pinkwart (Berlin)

Researchers: Benjamin Paaßen (Bielefeld), Sebastian Groß (Berlin)

Administration: Giesela Weitekemper (Bielefeld), Eva Sandig (Berlin)

Associates: Haibo He (Rhode Island)

Summary:

The rapidly increasing availability of online educational resources and the recent attention gained by distance education methods such as massive open online courses (MOOCs) show the importance of intelligent tutoring systems (ITS) as adaptive educational technologies that can personalize e-Learning. Classical ITSs require an exact  formalization of the learning task and learner-system interactions. Hence their applicability is typically limited to well-defined domains. In addition, their labor-intensive preparation
restricts their use to static, large-scale applications where development costs do not play a significant role. Within the first period of the FIT project, we have developed a FIT ITS infrastructure which allows the construction of ITSs in ill-defined domains based on machine learning techniques. In particular, we have developed prototype-based machine learning models for structures and structure-metric adaptation techniques which enable an autonomous organization of an ITS solution space in ill-defined domains, based on which feedback provision strategies can be grounded.
So far, the developed machine learning techniques are restricted to single tasks, and feedback provision is not tailored to individual users and their progress. The goal of DynaFIT is to (i) develop machine learning models which can generalize across different tasks and user behaviors and, based thereon, (ii) to enhance FIT ITSs via dynamic user-adaptive feedback and open learner models in ill-defined domains.
More specifically, we will develop cross-task dimensionality reduction (DR) techniques for structures which generate a task-independent representation of different solution spaces in one common latent space. This enables autonomous information transfer across tasks and a generalization from possibly singular user behavior to relevant underlying principles. This representation constitutes a key prerequisite for obtaining the following central components of DynaFIT: a visualization of relevant characteristics of solution spaces and learner behaviour (open learner models); a representation of user behaviour across tasks as low dimensional time series, for which classical data analysis techniques as well as relevance learning as developed in the first period of the FIT project are available; finally, based thereon, dynamic feedback provision strategies adjusted to this time series data. In this realm, we will exemplarily investigate dynamic peering strategies (highly relevant for larger online courses) in detail.
In addressing information transfer by cross-task dimensionality reduction, DynaFIT  contributes to a central topic of the SPP: the autonomous development of suitable representations for learning. Further, the envisioned enrichment of ITSs in ill-defined domains by dynamic feedback provision and open learner models bears great potential for highly dynamic large-scale educational technology facilities such as MOOCs.