- Questioning Learning Analytics – Cultivating critical engagement (LAK’22)
Gist of LAK 22 paper
- Competency 8.1
Competency 8.1: Prepare data for use in LightSIDE and use LightSIDE to extract a wide range of feature types.For the purpose of this exercise, I created a simple data set of three types of plants: vegetables, fruits and flowers. I classified text (taken from Wikipedia) based on the three categories. It looked like this:
I loaded ...
- Week 8 Activity – Data preparation
Activity: Textual data pre-processing and informal analysis
I created a list of positive words (unigrams and bigrams) from the given data and used them to identify positive and negative instances.
IF (effective OR intriguing OR breathtaking OR captivated OR (NOT not)_perfect OR loved OR real_chemistry OR really_good OR charm OR enthralled OR beautifully_done OR thoughtprovoking OR ...
- Week 6 Activity
In the activity for Week 6, we were asked to calculate different metrics for assessing models which were discussed in Ryan Baker’s unit of Behavior Detection and Model Assessment. Two data sets, classifier-data-asgn2.csv and regressor-data-asgn2.csv were given.
I used Excel for these calculations and for the last metric (A’ or AUC), I downloaded a plugin called XLSTAT from http://www.xlstat.com/en/ since ...
- Competency 7.4
Competency 7.4: Describe how models might be used in Learning Analytics research, specifically for the problem of assessing some reasons for attrition along the way in MOOCs.
One particular model described by Dr. Carolyn talks about how certain properties of discussion correlate to dropout in MOOC. It explores how analyses of sentiment predict attrition over time ...
- Competency 7.3/ Assignment
Building a simple text classification experiment – Training and evaluating a simple predictive model
I used LightSIDE tool as explained by Dr. Carolyn to run a simple classification experiment. The tool is easy to use and straightforward if we follow the steps.
In the Extract Features pane, I loaded the NewsgroupTopic dataset from the sample data directory ...
- Competency 7.1/ 7.2 Text Mining
Text Mining is the process of extracting and identifying useful and meaningful information, from different sources of unstructured text data.
Prominent Areas of Text Mining
Information Retrieval is the process of searching and retrieving the required document from a collection of documents based on the given search query. The search engines we use like Google, Yahoo ...
- Competency 6.2: Key Diagnostic Metrics
A part of Week 6 was designed to learn about the diagnostic metrics, to see how well our model does, as either classifiers or regressors.
Metrics for Classifiers
The easiest measure of model goodness is accuracy. It is also called agreement, when measuring the inter-rater reliability.Accuracy = # of agreements/ Total # of assessmentsIt is generally not ...
- Competency 6.1: Engineer both feature and training labels
My notes/ learning
Behavior detectors are automated (predictive) models that can infer from log files whether a student is behaving in a certain way.
-gaming the system by trying to succeed without learning
-carelessness by giving wrong answer even when having the required skills
-WTF behavior – Without Thinking Fastidiously (by doing unrelated tasks while using ...
- Competency 5.1/ Week 5 Activity
Competency 5.1: Learn to conduct prediction modeling effectively and appropriately.
I think this competency can be achieved if we are able to complete the given activity in RapidMiner. It is quite difficult for a newbie, but its well-described in the course and definitely doable 🙂
We were asked to build a set of detectors predicting the variable ONTASK for ...
- Competency 5.2/ Week 5 Reflection
A quick intro – I skipped weeks 3 & 4 for the time being since I was very much behind schedule coz of starting late. I jumped to Week 5 – Prediction modeling so that I can participate in the discussion and bazaar but sadly I was still lagging to participate. I’m aiming to complete ...