ALA-Reader
ALA-Reader
The last edition of this page was on: 2014/11/11
The Completion level of this page is : Low
The last edition of this page was on: 2014/11/11 The Completion level of this page is : Low
SHORT DESCRIPTION
Quote: Here is a software tool that can translate written text summaries directly into proximity files (prx) that can be analyzed by Pathfinder KNOT. It also generates text proposition files that can be imported by CMAP Tools to automatically form concept maps from the text. It should be of use to researchers who want to visualize "text" for various instructional and research-related reasons. Also it should work with different languages.
ALA-Reader contains a rudimentary scoring system. Essentially, this tool converts the written summary into a cognitive map and then scores the cognitive map using an approach that we developed for scoring concept maps. The "score" produced is percent agreement with an expert referent. As I narrow down what algorithms work, then I plan to release updated versions periodically.
TOOL CHARACTERISTICS
Usability
Tool orientation
Data mining type
Manipulation type
IMPORT FORMAT :
EXPORT FORMAT :
Tool objective(s) in the field of Learning Sciences | |
☑ Analysis & Visualisation of data |
☑ Providing feedback for supporting instructors: |
Tool can perform:
- Data extraction of type:
- Transformation of type:
- Data analysis of type:
- Data visualisation of type: (These visualisations can be interactive and updated in "real time")
ABOUT USERS
Tool is suitable for:
Required skills:
STATISTICS: Basic
PROGRAMMING: N/A
SYSTEM ADMINISTRATION: Basic
DATA MINING MODELS: Medium
FREE TEXT
Tool version : ALA-Reader (blank line) Developed by : |
SHORT DESCRIPTION
Quote: Here is a software tool that can translate written text summaries directly into proximity files (prx) that can be analyzed by Pathfinder KNOT. It also generates text proposition files that can be imported by CMAP Tools to automatically form concept maps from the text. It should be of use to researchers who want to visualize "text" for various instructional and research-related reasons. Also it should work with different languages.
ALA-Reader contains a rudimentary scoring system. Essentially, this tool converts the written summary into a cognitive map and then scores the cognitive map using an approach that we developed for scoring concept maps. The "score" produced is percent agreement with an expert referent. As I narrow down what algorithms work, then I plan to release updated versions periodically.
TOOL CHARACTERISTICS
Tool orientation | Data mining type | Usability |
---|---|---|
This tool is designed for general purpose analysis. | This tool is designed for Text mining. | Authors of this page consider that this tool is rather easy to use. |
Data import format | Data export format |
---|---|
. | . |
Tool objective(s) in the field of Learning Sciences | |
☑ Analysis & Visualisation of data |
☑ Providing feedback for supporting instructors: |
Can perform data extraction of type:
Can perform data transformation of type:
Can perform data analysis of type:
Can perform data visualisation of type:
(These visualisations can be interactive and updated in "real time")
ABOUT USER
Tool is suitable for: | ||||
Students/Learners/Consumers:☑ | Teachers/Tutors/Managers:☑ | Researchers:☑ | Organisations/Institutions/Firms:☑ | Others:☑ |
Required skills: | |||
Statistics: BASIC | Programming: | System administration: BASIC | Data mining models: MEDIUM |
OTHER TOOL INFORMATION
ALA-Reader |
http://www.personal.psu.edu/rbc4/score.htm |
Quote: Here is a software tool that can translate written text summaries directly into proximity files (prx) that can be analyzed by Pathfinder KNOT. It also generates text proposition files that can be imported by CMAP Tools to automatically form concept maps from the text. It should be of use to researchers who want to visualize "text" for various instructional and research-related reasons. Also it should work with different languages.
ALA-Reader contains a rudimentary scoring system. Essentially, this tool converts the written summary into a cognitive map and then scores the cognitive map using an approach that we developed for scoring concept maps. The "score" produced is percent agreement with an expert referent. As I narrow down what algorithms work, then I plan to release updated versions periodically. |
General analysis |
Researchers |
Basic |
N/A |
Basic |
Medium |
Text mining |
Data analysis, Data visualisation |
rather easy to use |
Low |