09.B. Measuring the learning rate of students

Information theory gives us the tools to measure the amount of information of a message (in bits) and the information rate (in bits/s) when sending a message over a channel. Additionally, it enables to measure the impact of perturbations (using mutual information) which occur when sending the message from a source to a receiver.

The aim of this thesis it to setup a theory, similar to the information theory, but where

  1. the information (in bits) is replaced with the knowledge graph,
  2. the source becomes the domain knowledge graph representing the knowledge of the teacher/the course,
  3. the receiver is the student that needs to learn the knowledge up to a certain level, and
  4. the channel which represents the various ways of communicating the knowledge through written documents, lectures (speech), video’s, examples.

The channel might be corrupted by errors. Hence, the channel is equivalent to the noisy channel in information theory.

Domain knowledge graph

The goal of the thesis is to develop the tools and techniques to quantify the amount of learned knowledge, i.e. how much of the course’s knowledge graph is effectively communicated to the student’s knowledge graph over the (possibly multiple) teaching channels. As it is impossible to directly measure the student’s knowledge graph in his/her brain, we must resort to measure this student’s knowledge graph through questions and answers.

Bloom’s taxonomy

An addition issue, which is also not present in information theory, is that different levels of understanding are possible. In information theory, one only has “a bit” as a communication unit, while knowledge can be understood in various levels. These levels are e.g. described in Bloom’s taxonomy and need to be included in the measurement of the amount of knowledge learned by the student.

2019
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