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Peer Review

Orange Data Mining - Data Mining

Reviewed:
Mar 6, 2020 by Computer Science
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Ratings

Overall Rating: 4.8 stars
Content Quality: 5.0 stars
Effectiveness: 4.8 stars
Ease of Use: 4.5 stars
Overview:

This website provides access to downloading an open source machine learning and data visualization desktop application appropriate for novices as well as useful for experts. The application addresses interactive data analysis workflows and includes an externsive toolbox.

Type of Material:

Development Tool, Support Videos, and Lecture Notes

Recommended Uses:

This tool is best used as support for a course and is appropriate for both in-person and online courses.

Technical Requirements:

Tool requires Microsoft Windows, Apple OS X, LInux, and/or appropriate installation of Python.

Identify Major Learning Goals:
  • Simple data analysis with data visualization
  • Visualization and manipulation of statistical distributions
  • Interactive data exploration
Target Student Population:

College General Ed, College Lower Division, College Upper Division, Graduate School

Prerequisite Knowledge or Skills:

Background knowledge of data science and data mining.

Fundamentals of computing, information representation, andprogramming.

Mathematical underpinnings in statistics and data science.

Content Quality

Rating:
Strengths:

This tool is useful for teaching data mining in particular and machine learning in general.

It provides comprehensive features for students to explore data mining and to conduct data analysis.

The content is of high quality with good documentation.

In includes the followings:

  • Addresses common data workflows
  • Extensive collection of data manipulation and visualization tools
  • Appropriate for data science and data mining coursework
  • Approach is appropriate for students in associated courses

Potential Effectiveness as a Teaching Tool

Rating:
Strengths:
  • Documentation articulates learning goals and provides links to associated resources
  • Use of YouTube tutorials and visualization components increases engagement and thus the potential for student learning
  • Inclusion of workflow examples and extensive catalog of workflow components, in conjunction with tutorials, promotes conceptual understanding
  • The application affords use in live demonstrations and both self-directed and guided explorations to achieve learning goals
  • It can be used in lectures and projects. For example, various machine learning models can be demonstrated and tested through a graphical interface.
  • The tool can facilitate the learning of data mining and it reinforces concept understanding (e.g., through hands on experience).
  • The learning goals are clear and the tool can enhance student learning in various aspects.

Ease of Use for Both Students and Faculty

Rating:
Strengths:
  • Ample instructions are provided for accessing, installing, and using the application
  • The website provides a clear and consistent layout
  • The website is easy to navigate
  • The website is in working order
  • The software appears to be stable and maintained
Concerns:

The complexity of the interface may be an impediment to new users. Such complexity may be inherent in the current state of the domain and the application does appear to increase the accessibility of the underlying tools.

Some interaction with the software may not yet be fully implemented (as of version 3.24.1); for example, the message, "There is no documentation for this widget."

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