Posts

  • Questioning Learning Analytics – Cultivating critical engagement (LAK’22)
    Gist of LAK 22 paper
  • Competency 3.1 – Basics of Social Network Analysis
    I’m going back to Weeks 3 and 4 to learn about Social Network Analysis since the course is nearing completion. I will go back to the final wrap up Week 9 after I finish these two weeks’ lessons. Competency 3.1: Define social network analysis and its main analysis methods. Social Network Analysis (SNA) provides insights into how different social ...
  • Competency 8.5
    Competency 8.5: Examine texts from different categories and notice characteristics they might want to include in feature space for models and then use this reasoning to start to make tentative decisions about what kinds of features to include in their models. I tried the Bazaar activity in Prosolo (but my myself since I ...
  • Competency 8.3/ 8.4
    Competency 8.3: Compare the performance of different models. I compared two models, one from a unigram only feature set and the other from a unigram, bigram and trigram feature set using my test data set. I was at first using the Newsgroup data set as suggested in the Prosolo assignment, but some options were not working ...
  • Competency 8.2
    Competency 8.2: Build and evaluate models using alternative feature spaces. I used the different feature spaces that I saved in the previous exercise for building models. My data set was very small and I intended to use it just for testing. I found significant improvement in metrics while comparing the models of POS features Vs Unigrams ...
  • 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 Rule 1: 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: 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 ...