Dartmouth MOOC

An overview of course participants’ discussion contributions in Dartmouth Introduction to Italian Opera MOOC course (link to the course)

The discussion contribution is defined as:

  • posting a new thread,
  • replying to a new post, and
  • commenting on a reply

https://public.tableau.com/profile/publish/MUSX/MUSX

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An overview of user participation results in Dartmouth Environmental Science MOOC course – Introduction to Environmental Science:

https://public.tableau.com/profile/publish/ENVSXEvents/ENVSXQuizEvent

For the purpose of this study, we defined quiz submissions, video views and discussion contributions as indicators of MOOC activity engagement. The number of each activity conducted by individual participants was collected. We compared participant engagement results derived from ENVSX first run and second run. The preliminary result shows that the engagement rates of quiz submissions and video plays in ENVSX second run are significantly greater than those engagement rates yielded in ENVSX first run, even though there were less participants in second run. Furthermore, higher percentage of course participants in ENVSX second run contributed discussion activity compare to ENVSX first run and the discussion engagement rate (average #new posts/replies/comments per participant) in ENVSX second run is also higher.

The result suggests that ENVSX second run is more successful in engaging students in course activities, including quiz submissions, video views and discussion contributions respectively.

  • quiz engagement rate = total #quiz submissions / #unique participants
  • video engagement rate = total #video plays/#unique participants
  • discussion engagement rate = total #discussion contributions/#unique participants

 

ENVSX——————————————————————————————————–

An overview of user participation results in Dartmouth Engineer Science MOOC course -The Engineering of Structures Around Us:

  • Quiz Submissions
  • Video Plays
  • Discussion Contributions

https://public.tableau.com/profile/publish/ENGSXEvents/ENGSXDiscussionContribution

edX

Recent Posts

Predictive Modeling: Using logistic regression to identify students who need help early on

Logistic regression method has been commonly used to predict the class of observations, which have only two possible outcome values: 0 or 1, yes or no, based on predictor variables.

The example in this blog is intended to demonstrate how we can employ the method to identify potential indicators and model a binary outcome, which predicts fail or pass for any graded assignments at a given time. For the sake of simplicity, in this blog, we started with the most common data points that can be harnessed from an LMS:
•Submission time – how early or late an assignment was submitted by a given student in relation to the due time of the assignment,
•total activity time – accumulative time spent in a course at a given time,
•total number of page views – accumulative number of clicks on course content, and
•the number of late submissions.

In this demo, student’s current score was converted to a binary value, pass or fail, as the response variable.

Using the logit model

The code below estimates a logistic regression model using the glm (generalized linear model) function in R. Based upon the initial explorations of the sample data, we decided to convert number of late submissions to a factor to indicate that it should be treated as a categorical variable. It should also work if you treat it as a continuous variable.

 glm(formula = score_status ~ submission_duetime_diff + late_submission + 
    total_activity_time + page_views, family = "binomial", data = df)

You can also use predicted probabilities to examine the model. Predicted probabilities can be computed for both categorical and continuous predictor variables. It is always helpful to use graphs of predicted probabilities to present the model. Below is a plot with the predicted probabilities pass or fail against indicator variable colored by late submission status.

Gathering more variables and repeating the process

Once you get a prototype working, you can then add more variables, such as discussion participation and content access. In this demo, we used degree centrality to generate a score for an individual learner based upon their discussion interaction activities. We quantified the content access by summing the total clicks on each content category, i.e., files, assignments, modules, discussions.

Degree Centrality: The size of each nodes corresponds to the number of interactions, the red node yields the highest degree (in and out) centrality score, and the arrow of each link denotes that direction of the interaction

Displaying the predictive outcome in a meaningful layout

After all, the statistical analyses is to provide instructors with a visualization that presents the results in an user-friendly digestible format, which helps them make an informed decision to reach out to the student who was identified as an individual-may-need-help.

Resources: https://stats.oarc.ucla.edu/r/dae/logit-regression/

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