Best Moodle Innovation for 2019 -- Nominations

Nominations open until February 14, 2020 at 23:59

Award for Best Moodle Innovation for 2019 -- Nomination Form

Criteria for nomination:

  • Anyone, including the developer, can nominate the code for the recognition award.
  • Any individual can only make one nomination.
  • Any current member of MAJ, apart from those on the vetting committee are eligible to receive the award.
  • Please fill out all the information. Give a description of the innovation, why it benefits Moodle users (especially in Japan), and a link to its source code and documentation.
  • The R&D committee will determine the awardees, based on the panel's expertise.

These awards to be presented at the coming MoodleMoot:

  • Best innovation for year 2019
  • 2nd Best innovation for year 2019
  • 2019 Honorable mention
Justin HUNT
Innovation Name:プラグインの名前:
P-CHAT
Innovation Description:目的・機能の説明:

The P-CHAT  (Practice Conversations as Holistic Assessment Tools) plugin is a formative assessment tool for student to student conversations in a language learning classroom. It provides students with:

  • An opportunity to practice conversational English
  • Immediate feedback based on hard data of their contribution
  • A timely opportunity to reflect on their performance with a view towards future improvement. 
The classroom exercise itself has been developed and used over the past two years using off the shelf tools with great results. P-CHAT is a purpose built implementation of that exercise as a Moodle activity plugin.

Teachers can use the plugin for student assessment and evaluation, and researchers can access a large quantity of data for analysis. A P-CHAT activity has 4 steps. 

  1. Students prepare to converse in English about a particular topic.
  2. The students have the conversation, and make an audio recording of their contribution. 
  3. Once the conversation has ended, students listen to their recording and manually transcribe their speaking. 
  4. When students are finished transcribing, they receive immediate feedback on their oral production including:
     total words spoken, turns taken, average turn length, longest turn length, target vocabulary used, and a comparison of their transcript against one generated using 
    automatic speech recognition (ASR). Students complete the activity by answering a series of reflective prompts.
 This plugin was being developed with support by JSPS KAKENHI Grant Number 19K13309. It is available for download and use at no charge. 
Download or GIT repository link:ダウンロード/GITリポジトリのリンク:
Demo Site:デモサイト:
Plugins Directory Link:Moodle プラグインディレクトリリンク
Screenshot:スクリーンキャプチャー: