Interactive learning tasks and their technical requirements to multimedia learning systems

Bremen 22. 04. 2004

Interactive learning tasks and their technical requirements to multimedia learning systems

Sebastian Rudolph& Hermann KörndleTU Dresden

© Psychologie des Lehrens und Lernens – TU Dresden – http://studierplatz2000.tu-dresden.de

Outline

Part I – learning tasks

definition and purposes

design principles

Part II – increase interactivity

on the importance of appropriate feedback

evaluating free answers via Conceptual Graphs


Definition

An interactive learning task fosters knowledge acquisition and consolidation by

focussing the user‘s attention to central contents,

invoking, guiding, and enforcing a learning activity in the user,

in case of difficulties, providing appropriate help that enables the user to continue solving the task.

It can be integrated into all phases of the learning process.


Integration of tasks into the learning process

Preparation

Evaluation Execution

Tasks

make requirements transparentactivate previous knowledgeguide attention

learning and practicing

apply strategiesuse processing hints

check learning success

scrutinize strategies


Construction decisions for complex interactive learning tasks

content specification of the topic content structure

operations specification of the cognitive operations to perform specification of the solution path

form presentation component reactive component interaction channels

interaction design of the interaction processes and feedback algorithms specification of additional information


Elaborated feedback: an example

field-tested task set for the advanced qualification in Latin

empiric error analyses

Example

wrong

right

task with same

source of error

task with new

source of error

righttask

with specific source of error

2nd correction attempt

1st correction attempt repeat right solution path

right

wrong

right

wrong

wrongIII. knowledge of correct response (KCR) und

knowledge on how to proceed (KH)• colored indication of error position• juxtaposition of correct vs. wrong result• acoustic presentation of the correct solution procedure

II. KR & knowledge about mistake (KM)• colored indication of error position• in case of systematic error: hint wrt. solution strategy• otherwise: hint by presenting worked-out example

I. knowledge of response (KR)

repeat right solution path


Complex tasks

combining „task primitives“, multimedial presentations and simulation environments enhances mental integration of the content

tasks referring to precisely defined and conceptually ordered terms could benefit from the application of ontologies

Example


How to increase interactivity even further?

until now: tasks with restricted answer formats advantage: automatic evaluation is easy disadvantage: by presenting choices, solving by

recognition is enabled disadvantage: the answer‘s structure is prescribed

desirable: introduction of tasks with free answers advantage: higher requirements to the student, who has

to structure the answer him-/herself disadvantage: automatic evaluation problematic


What about Conceptual Graphs?

invented by John F. Sowa based on Ideas of Charles S. Peirce. [J. Sowa: Conceptual Structures: Information Processing in Mind and Machine. Addison-Wesley, 1983]

purpose: semantically oriented storage, representation and processing of knowledge

aim: kind of representation being equally interpretable and processable by humans and computers

similarities to semantic networks in cognition psychology


Basic example

„Sebastian works on conceptual graphs in the TOOLKIT project.“

person: workOn topic:Agnt Thm

Inst

project:

• • conceptual relations• directed arcs

, consisting of and (optional) concept type referentcomposed from:

concepts

Sebastian ConceptualGraphs

Toolkit



Inst

project:


, consisting of and (optional) concept type referentcomposed from

concepts


Toolkit


Basic example



Inst

project:



concepts


Toolkit


Basic example



Inst

project:



concepts


Toolkit


Basic example



Inst

project:



concepts


Toolkit


Basic example


Complex example

„Tom believes Mary wants to marry a sailor. “


Sketch: How to handle results of tasks with free answers (1/2)

Reaktionspartner

entziehen

Wer/Was

Wem/Was Wen/Was

Oxidationsmittel

Elektronen


Reaktionspartner

entziehen

Wer/Was

Wem/Was Wen/Was

Oxidationsmittel

Elektronen

input graph

wegnehmen

Wer/Was

Wen/Was

Oxidationsmittel

Elektronen

collection of answers conceptual hierarchy

Sketch: How to handle results of tasks with free answers (2/2)


Documents

Interactive learning tasks and their technical requirements to multimedia learning systems