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blended-learner-data-science

Blended Learner Data Science (BLDS). Use-case for AppInventor

Analyse and visualize data from the appinventor course and the corresponding EDX server and AppInventor server data.

Requirements

Python 3, Pandas, Numpy, Colorspacious, Dill, Networkx, Pillow, Python_dateutil, Python_Lenvenshtein, Matplotlib, Seaborn, SKLearn, Textstat, Jupyter, PyGraphviz

For the packages installable through pip, you may use the requirements.txt to install them.

aggregated_sessions

Visualize appinventor sessions aggregated among all students.

click_frequencies

Visualize the number of days students have clickstream activity.

knowledge_type_transitions

Quanitfy how students move between knowledge types that resources contain.

levenshtein

Look at levenshtein similarity between the resources students use and the knowledge type categories they use.

link_edx_appinventor

Visualize, anonymize and link data between the EDX server and the AppInventor Server.

resource_timelines

Visualize resource usage for students over the time that the course is offered.

resource_transitions

Visualize how students move between resources.

reviews

Determine resource reviews based on two metrics.

scm_bky

Visualize aggregated SCM and BKY usage.

scm_bky_node_types

Visualize nodes used by students in BKY and SCM.

scm_bky_pairs

Visualize aggregated SCM-BKY transitions.

signup_dropout

Visualize aggregated signups and dropouts of students.

user_and_course_dfs

Generate necessary dataframes for other notebooks. Generates a serialized pickle file that the other notebooks load. Must run before other notebooks.

Run

You must modify the variables in config.ini, run user_and_course_dfs. To get the necessary resource data from the course, you must run https://github.com/18goldr/web-crawler.

By default, all files will display the graphs inline. To save the graphs to a file, you must modify the variable to_file in config.ini to be True. You may also modify to_file anywhere within the scripts. This allows to save to file on a per-graph basis and is easier than having to reload external modules in Jupyter/IPython.

References

@inproceedings{gold2020analyzing,
  title={Analyzing K-12 Blended MOOC Learning Behaviors},
  author={Gold, Robert and Hemberg, Erik and O'Reilly, Una-May},
  booktitle={Proceedings of the Seventh ACM Conference on Learning@ Scale},
  pages={345--348},
  year={2020}
}

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Blended Learner Data Science(BLDS). Use-case for AppInventor

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