tecnero.blogg.se - Insights to behavior

INSIGHTS TO BEHAVIOR CODE

The time spent tile shows the amount of time the student spent on the assignment in the middle, mapped to a log scale.īased on thousands of student submissions, we determined a generalized threshold of submission under 2 minutes had a high likelihood of being plagiarized (as represented by the red region). The first tile was inspired by teachers who already used Codio who reported to us that they often sort the student progress dashboard by time spent. The dashboard will only show tiles that are above or below their respective thresholds – so if you click on a low indicator you will see fewer tiles than if you click on a high indicator.Ĭhen and colleagues (2018) found that, on average, 5.5% of students cheat on asynchronous exams, with no more than 10% on any given exam (as shown in Chen, West & Zilles, 2018 - Figure 4, reproduced below).

There are three levels of the indicator: low, medium, and high.Ĭlicking on a behavior indicator opens the Behavior Insights dashboard, which consists of up to five tiles:

On the student progress screen, you can filter and sort students by their Behavior indicator. Detecting Plagiarism in Computing ExamsĬurrently, the indicators and corresponding dashboard are configured to detect plagiarism. Turn it on in your course under Grading > Basic Settings and click Save Changes. Ready to try it for yourself? It’s now live in Codio!

“Ignore” insights that you find unconcerning.

Click to see student pastes contextualized in their process.

Read data narratives below each tile providing interpretation support.

See raw metrics compared to threshold values, selected based on thousands of assignments.

Quickly sort students so you only review submissions that need it.

Introducing, Codio’s new Behavior Insights: Watwin, NPSM, RED, Error Qutient) seamlessly into real-world tools. Instead, we need to bring the promise of behavioral metrics being explored by researchers (e.g.

INSIGHTS TO BEHAVIOR CODE

While there are some tools that give a qualitative description of the programming process such as Codio’s own Code Playback which shows key-stroke level changes or ZyBooks’ Coding Trail feature which summarizes student code runs, no teacher has time to manually review all their student submissions – especially when teaching at scale. With the current wave of concern around ChatGPT in education, including recent work showing that MOSS can be fooled by Large Language Model (LLM) generated code (Biderman & Raff, 2022), it is beyond time for CS Education tools to emphasize students' process over product.

CS Education tools that emphasize students' process over product