Participation credit rewards and incentivizes contributions that help to build a constructive learning community for our study of time series analysis.

  1. The piazza site is the main venue for participation in class discussions. If this site does not currently contain the discussion topics you need, then start them. The instructors will also introduce discussion topics that you may like to respond to.

  2. Anonymous contributions generally build community less well than signed ones, so more credit will be given to non-anonymous contributions unless for some reason the topic requires anonymity.

  3. The principle of academic scholarship that is emphasized in homework assignments also applies to piazza posts. You can use outside sources when posting a question or responding to another student’s question, but proper attribution should be given to outside sources when used. If it is determined that your piazza posts are based on comments from outside sources that were not properly cited, you may receive negative participation points.

  4. ChatGPT—or other natural language models—may be used to answer piazza posts, but the student must indicate that such software was used. Furthermore, in order to recieve full participation points, the student must demonstrate how they went beyond simply entering a question on an existing piazza post as input into ChatGPT and posting the given output, or posting the first result obtained from a google search of the given question. This can be as simple as giving evidence of what they searched in order to get a correct solution, or summarizing what they found utilizing the termonology and language that is used in our class.

[2 points] piazza participation that meets or exceeds expectations.

[1 point] A contribution which does not quite meet expectations. For example, a small comment which turns out not to be helpful for starting or answering a class discussion. Probably in the least-well-constructed 10% of all class contributions.

[0 points] No contribution, or a contribution far below expectations.