Author: Shibani

Notes: Visualizing sequential patterns for text mining

Reference:

Wong, P. C., Cowley, W., Foote, H., Jurrus, E., & Thomas, J. (2000). Visualizing sequential patterns for text mining. In Information Visualization, 2000. InfoVis 2000. IEEE Symposium on (pp. 105-111). IEEE.

Background:

  • Mining Sequential patterns aims to identify recurring patterns from data over a period of time.
  • A pattern is a finite series of elements from the same domain A -> B -> C -> D
  • Each pattern has a minimum ‘support’ value which indicates the percentage of pattern occurrence. (E.g. 90% of people who did this process, did the second process, followed by the third process)
  • Sequential pattern vs association rule:
    • Sequential pattern – studies ordering/arrangement of elements E.g. A -> B -> C -> D
    • Association rule – studies togetherness E.g. A+B+C -> D

Purpose:

  • Presenting a visual data mining system that combines pattern discovery and visualizations.

Method:

Datasets:

Open source corpus containing 1170 news articles from 1991 to 1997 and harvested news of 1990 from TREC5 distribution.

Pre-processing:

  1. Topic Extraction: Identifies the topic in documents based on the co-occurrence of words. Words separated by white space evaluated – stemming done, prepositions, pronouns, adjectives, and gerunds ignored.
  2. Multiresolution binning: Bins articles with the same timestamp (E.g. Binning by day, week, month, year)

Discovery of sequential patterns by Visualization:

  • Plotting topics/ topic combinations over time.
  • Strength: Can quickly view overall patterns and individual occurrence of events.
  • Weakness: No knowledge on exact connections that make up the pattern and statistical support on the individual patterns.

Discovery of sequential patterns by Data mining:

  • Building patterns on n-ary tree with elements as nodes.
  • Patterns are valid if the support value is greater than threshold.
  • A sample pattern mining from given input data is given in Figure 2 of the paper.
  • Strength: Provides accurate statistical (support) values for all weak and strong patterns.
  • Weakness: Loses temporal and locality information, large number of patterns produced in text format making human interpretation harder.

Visual Data Mining system:

visual-data-mining

  • Combining visualization and data mining to compensate each others’ weaknesses (Refer Figure 4 & 5 in the paper to see the pattern visualizations).
  • Binning resolution can be changed to see different patterns based on day, week, month, year etc.
  • Patterns associated to a particular topic can be picked.

Result/Discussion:

  • Strength of pattern is not easily identifiable from the visualization without statistical measures. Pattern mining gets enhanced by graphical encoding with spatial and temporal information.
  • Knowledge discovery by humans is aided by combining statistical data mining and visualization.

Future Work:

  • Handling larger data sets using secondary memory support and improve display.
  • Integrating more techniques like association rules into visual data mining environment.

Notes: Discipline-independent argumentative zoning

Reference:

Teufel, S., Siddharthan, A., & Batchelor, C. (2009, August). Towards discipline-independent argumentative zoning: evidence from chemistry and computational linguistics. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3-Volume 3 (pp. 1493-1502). Association for Computational Linguistics.

Background:

  • Argumentative Zoning (AZ) classifies each sentence into one of the categories below (inspired by knowledge claim KC) of authors :
    • Aim, Background, Basis, Contrast, Other and Textual.
[Refer AZ scheme – Teufel, S., Carletta, J., & Moens, M. (1999, June). An annotation scheme for discourse-level argumentation in research articles. In Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics (pp. 110-117). Association for Computational Linguistics.]

Purpose:

  • Establishing a modified AZ scheme AZ-II with fine grained categories (11 instead of 7)  to recognize structure and relational categories.
  • Experimenting annotation using AZ scheme in two distinct domains: Chemistry and Computational Linguistics (CL).
  • Testing an annotation scheme to systematically exclude prior domain knowledge of annotators.

Method:

Annotation:

  • Domain independent categories so that the annotations can be done based on general, rhetorical and linguistic knowledge and no scientific domain knowledge is necessary.
  • Annotators are semi-informed experts following the rules below so that the existing domain knowledge has minimalist interference with annotations:
    • Justification is required for all annotations based on text based evidence such as cues, and other linguistic principles.
    • Discipline specific generics are provided based on high level domain knowledge so that the annotators can identify the validity of knowledge claims made in the domain (E.g. a “Chemistry primer” with high level information regarding common scientific terms to help a non-expert).
    • Guidelines are given with descriptions for annotating the categories; some categories might require domain knowledge for distinguishing them (e.g. Authors mentioning about the failure of previous methods: OWN_FAIL vs ANTISUPP, Reasoning required to come to conclusions from results: OWN_RES vs OWN_CONC).

Experiment:

  • Data:
    • Chemistry – 30 journal articles, 3745 sentences
    • CL – 9 conference articles, 1629 sentences
  • Independent annotations using web based tool. Refer example annotations in appendix of the paper.

Results:

  • Inter-annotator agreement: Fleiss Kappa coefficient, κ = 0.71 for Chemistry and κ = 0.65 for CL.
  • Wide variation in the frequency of categories –> fewer examples for supervised learning for rare categories (Refer ‘Figure 3: Frequency of AZ-II Categories’ in the paper to see the frequency distinctions between the two domains).
  • Pairwise agreement calculated to see the impact of domain knowledge between annotators: κAB  = 0.66, κBC  = 0.73 and κAB  = 0.73 –> Largest disagreement between expert (A) and non-expert (C).
  • Inter-annotator agreement to see the distinction between categories: κbinary = 0.78 for chemistry and κbinary = 0.65 for CL –> Easier distinction of categories in Chemistry than CL.
  • Krippendorff’s category distinctions to see how a category falls apart from the other collapsed categories: κ=0.71 for chemistry, κ=0.65 for CL
    • Well distinguised: OWN MTHD, OWN RES and FUT
    • Less distinguised: ANTISUPP, OWN FAIL and PREV OWN –> troubleshooting required for guidelines
  • Comparison of AZ-II to original AZ annotation scheme by collapsing into 6-category AZ annotation: κ=0.75 –> annotation of high consistency.

Discussion:

  • Positive result for domain independent application of AZ scheme and training non experts as annotators.
  • Annotating more established discipline like Chemistry was easier than CL.

Future Work:

  • Automation of AZ annotation
  • Expanding annotation guidelines to other disciplines and longer journal papers.

 

 

 

Notes: Mover – a Machine Learning tool to analyze technical research papers

Reference: Anthony, L., & Lashkia, G. V. (2003). Mover: A machine learning tool to assist in the reading and writing of technical papers. IEEE transactions on professional communication, 46(3), 185-193.

Background:

  • Identifying the structure of text helps in reading and writing research articles.
  • The structure of research article introductions in terms of moves is explained in the CARS model (Ref: J. M. Swales, “Aspects of Article Introductions,” Univ. Aston, Language Studies Unit, Birmingham, UK, Res. Rep. No. 1, 1981.).

Problem:

  • Identifying the moves in a particular type of article.
  • Time-consuming identification of moves by raters (manual annotation) with no immediate feedback.

Purpose:

  • To provide immediate feedback on move structures in the given text.

Method:

  • Using supervised learning to identify moves from 100 IT research article (RA) abstracts.
  • Machine readable abstracts were further pre-processed with subroutines to remove irrelevant characters from raw text.
  • Data labelled based on the modified CARS model which had three main moves with further steps under each move as below: (Ref: L. Anthony, “Writing research article introductions in software engineering: How accurate is a standard model?,” IEEE Trans. Prof. Commun., vol. 42, pp. 38–46, Mar. 1999.)
    1. Establish a territory
      1. Claim centrality
      2. Generalize topics
      3. Review previous research
    2. Establish a niche
      1. Counter claim
      2. Indicate a gap
      3. Raise questions
      4. Continue a tradition
    3. Occupy the niche
      1. Outline purpose
      2. Announce research
      3. Announce findings
      4. Evaluate research
      5. Indicate RA structure

 

  • Supervised Learning System – Implementation details:
    • Bag of clusters representation was implemented:
      • Dividing input text into clusters of 1-5 word tokens to capture key phrases and discourse markers as features.
      • Bag of words model does not consider word order and semantics by splitting input text into word tokens – not useful in the discourse level.
      • E.g. “Once upon a time there were three bears” clusters –> “once”, “upon”, “once upon”, “once upon a time”
      • Not useful clusters (noise) removal using statistical measures – Information Gain (IG) scores used to remove clusters below threshold.
      • ‘Location’ feature added to take note of preceding and later sentences – position index of sentence  in the abstract.
        • Additional training feature for the classifier – probability of common structural step groupings.
    • Naive Bayes learning classifier outperformed other models.
    • Tool available for download as AntMover.

Results:

  • Evaluation of Mover:
    • Training: 554 examples, Test: 138 examples
    • Five fold cross validation, Average accuracy: 68%
    • Classes (steps within the structural moves) with few examples had lower accuracy. Incorrectly classified steps were mostly from the same move (note similarity among 3.1, 3.2. 3.3)
    • Features to improve accuracy:
      • When two most likely decisions are used (instead of predicting only one class) using the Naive Bayes probabilities, accuracy increased to 86%.
      • Flow optimization effectiveness improved accuracy by 2%.
      • Manual correction of steps adding new training data (second opinion of students on the moves classified by the system used for retraining the model).

Discussion:

  • Based on two practical applications in classroom, the usage of ‘Mover’ assisted students to
    • identify unnoticed moves in manual analysis.
    • analyze moves much faster than manual analysis.
    • better understand own writing and prevent distorted views.
  • Implications:
    • Important vocabulary can be identified for teaching from the ordered cluster of words.
    • Trained examples can be used as exemplars.
    • Aid for immediate analysis of text structure.
  • Future Work:
    • Increasing the accuracy of Mover.
    • Expanding to more fields – currently implemented for engineering and science text types.

 

Notes: NLP Techniques for peer feedback

Reference: Xiong, W., Litman, D. J., & Schunn, C. D. (2012). Natural language processing techniques for researching and improving peer feedback. Journal of Writing Research, 4(2), 155-176.

Background:

  • Feedback on writing is seen to improve students’ writing, but the process is resource intensive.
  • Possible options to reduce the workload in giving feedback:
    • Direct feedback using technology assisted approaches (from grammar checks to complex computational linguistics).
    • Peer Review [Considered in this paper].
  • Peer review:
    • Good feedback from a group of peers is found to be as useful as the instructor’s feedback and even weaker writers are seen to provide useful feedback to stronger writers (See references in original paper).
    • When providing feedback on other students’ work, students become mindful of the mistakes and improve their own writing.
    • Some web-based peer review systems: PeerMark in turnitin.com, SWoRD (used in this study) and Calibrated Peer Review.

Problem:

  • Challenge lies in the form of feedback provided by peers – peer feedback might not be in a form useful to make revisions. Key features identified to aid revisions:
    1. Localized information (Providing exact location details like paragraph, page numbers or quotations).
    2. Concrete solution (Suggesting possible solution rather than just pointing the problem).
  • Research problem: Studying peer review is hard with a large amount of feedback data.
  • Practical problem: Identifying useful feedback for students and possible interventions to help them provide good feedback.

Purpose:

  • To automatically process peer feedback and identify the presence or absence of the two key features (Providing feedback on feedback for students and automatically coding feedback for researchers).
  • Refer prototype shown in figure 1 of the original paper that suggests students to provide localized and explicit solutions.

Technical – How? (Details explained in study 1 and study 2)

  1. Building a domain lexicon from common unigrams and bigrams in student papers.
  2. Counting basic features like domain words, modals, negations, overlap between comment and paper etc. from each feedback.
  3. Creating a logic model to identify the type of feedback (Contains localization information or not/ contains explicit solution or not) – classification task in machine learning.

Method and Results:

Study 1 – Localization Detection:

  • Each feedback comment represented as a vector of the four attributes below:
    1. regularExpressionTag: Regular expressions to match phrases that use location in a comment (E.g. “on page 5”).
    2. #domainWord: Counting the number of domain-related words in a comment (based on the domain lexicon gathered from frequent terms in student papers).
    3. sub-domain-obj, deDeterminer: Extracting syntactic attributes (sub-domain-obj) and count of words like “this, that, these, those” which are demonstrative determiners.
    4. windowSize, #overlaps: Extracting the length of matching words from the document to identify quotes (windowSize) and words overlapped.
  • Weka models to automatically code localization information. The decision tree model had better accuracy (77%, recall 82%, precision 73%) in predicting if a feedback was localized or not. To refer the rules that made up the decision tree, take a look at Figure 2 of the original paper.

Study 2 – Solution Detection:

  • Feedback comment represented as vectors using the three types of attributes(Refer table 2 in the original paper for details).
    • Simple features like word count and the order of comment in overall feedback.
    • Essay attributes to capture the relationship between the comment and the essay and domain topics.
    • Keyword attributes semi-automatically learned based on semantic and syntactic functions.
  • Logistic Regression model to detect the presence/ absence of explicit solutions (accuracy 83%, recall 91%, precision 83%). Domain-topic words followed by suggestions were highly associated with prediction. Detailed coefficients of attributes predicting presence of solution can be referred in Table 3 of the original paper.

Study 3: Can Research Rely on Automatic Coding?

  • Comparing automatically coded data to hand coded data to see if the accuracy is sufficiently high for practical implementation.
  • Helpfulness ratings by peers and 2 experts (content, writing experts) on peer comments at a review level.
  • To account for expert ratings:
    • Regression analysis using feedback type proportions (praise only comments, summary only comments, problem/solution containing comments), proportion localized critical comments, and proportion solution providing comments as predictors.
    • 10 fold cross validation – SVM best fit.
    • To check if same models are built using machine coded and hand coded data – 10 stepwise regressions. Refer Table 4 in the original paper to see the feedback features commonly included in the model by the different raters – Different features were helpful for different raters.
    • Overall regression model is similar to hand coded localization data (Most of the positivity, solution and localization were similar between hand coding and automatic coding).

Discussion:

  • Predictive models for detecting localization and solution information are statistical tools and do not provide deep content insights.
  • To be integrated into SWoRD to provide real time feedback on comments.
  • Technical note: Comments were already pre-processed – segmented into idea units by hand; data split by hand into comment type (summary, praise, criticism).
  • Future work:
    • Examine impact of feedback on feedback comments
    • Obtaining generalization across courses
    • Improving accuracy of prediction

 

Notes: The calibration of student judgement through self-assessment

Reference: Boud, D., Lawson, R., & Thompson, D. G. (2015). The calibration of student judgement through self-assessment: disruptive effects of assessment patterns. Higher Education Research & Development, 34(1), 45-59.

Background:

  • Effective judgement of own work is an important attribute for HDR students
  • Focused on Self-Assessment (also represented as Self-regulation and Metacognition in few works)

Problem:

  • Self assessment is not facilitated using systematic activities, but rather thought to be achieved from normal tasks.
  • Students are not given feedback on their judgement.
  • Ensure that different assessment methods don’t distract students’ ability of learning and judgement.

Purpose:

  • To investigate if students’ judging improves over time through criteria-based self-assessment in the given units of study in two parts:
    1. Replicate improvement of student judgement over time with more data from different disciplines (Repeat questions 1- 4)
    2. Investigate improvement of judging skill over sequential modules and analyze based on assessment type, assessment criteria (New questions 1-4)

Method/ Context:

  • Voluntary self assessment of students in authentic settings using an online assessment system ReView™.
  • Percentage marks from continuous sliding scale stored – of both students and tutors.
  • Data from 5 year period in Design, and 3 year period in Business course from two Australian universities.
  • 182 design students, 1162 business students.

Results and Discussion:

Repeat Q1: Does accuracy in students’ self-assessed grades vary by student performance?

  • Ability level – High (distinction or high distinction), low (fail) and mid (pass or credit)
  • p values
  • Yes. Low ability students did not improve judgement over time. Significantly higher improvement for mid ability students (Students matched tutors grading)

Repeat Q2: Does students’ accuracy in self-assessment relate to improved performance?

  • Accuracy levels: Over-, accurate and Under-estimators
  • One way ANOVA
  • Accurate estimators had increased scores over time

Repeat Q3: Do students’ marks converge with tutors’ within a course module?

  • Series of paired t-tests
  • Convergence found in design data, but not in business data
    • Design assessment tasks are scaffolded to lead one to other, but business uses different modes of assessment within a course module.

Repeat Q4: Does the gap between student grades and tutor grades reduce over time?

  • Yes. Students’ judgement improves over the time of the degree programme, but not very useful as the convergence happens almost at the end of the degree programme.

New Q1: Does the gap between student grades and tutor grades reduce across modules designed as a sequence?

  • No data for design and reduction in gap (erratic patterns with no gradual reduction) between student and tutor grades for business with sequential modules
  • Leading to examine the type of assessments

New Q2: Does mode/type of assessment task (e.g., written assignment, project, and presentation) influence students’ judgement of grades?

  • Data was inconsistent, despite showing earlier convergence for few assessment types. Most assessment types saw convergence in iterations 2 or 3 (Refer Table 1 from the original paper).
    • Could be due to difference in tasks within each assessment type

New Q3: Does analysis of criteria that relate to type of assessment task influence students’ judgement of grades?

  • Consistent and related criteria for particular assessment type fosters faster calibration in iterations 1 or 2 (Refer Table 2 from the original paper).

Limitations:

  • Criterion is provided for assessment since students are not experts, however a holistic evaluation of own work is recommended.
  • Not possible to identify the cause for improvement from independent measures.
  • Whole population of students is not included, especially the less engaged students who might be low achieving.
  • There might have been other informal factors (not measured in this study) to influence the results like the following: comments received from staff, discussions with peers, and students’ own aspirations.

 

Critical analysis for researchers

A critical approach is often needed for researchers when it comes to reading, writing and analysis of research articles. Being critical is not to find faults, but rather to ask questions and evaluate the reliability of what is stated. Here’s some useful information from a UTS session on ‘Critical Analysis – What is it and Why is it important for Researchers?’ 

  • There’s often not one absolute right or wrong answer, or one correct interpretation.
  • Pay attention to:
    • Can you make an outline of it?
    • Can you identify the evidence or reasoning that supports it?
    • Are they supporting it, challenging it or simply stating it?
    • What sort of language do they use?
    • How the paper’s argument has been structured
    • How the authors discuss or refer to the work of others
    • How diagrams and other non-verbal elements (if any) have been used
  • Ask questions. It is important to question the evidence behind arguments, ideas and decision making
  • Aim to address most, if not all of the below questions for your research: (Source: https://www.plymouth.ac.uk/uploads/production/document/path/1/1710/Critical_Thinking.pdf):

critical-thinking-model

critical-thinking-qns

Some practical advice for critical writing

  • Move beyond describing knowledge and facts to questioning, analysing and evaluating knowledge.
  • Put boundaries to your research.
  • Acknowledge what remains problematic or unresolved (Limitations/ Future Work).
  • Provide claims with support, do not generalise (E.g. “Most authors agree..” – Who are these authors?).
  • Stick with the same terminology throughout all chapters.
  • Put evidences in tables to highlight them and to avoid descriptive text.
  • Give signals to readers and help them read through – by outlining mini introductions for each section and signposting to move from one chapter to another.
  • Identify the key arguments and relationships between authors in your literature review.
  • When integrating arguments from different sources, paraphrase them.
  • Support your key arguments with more references.
  • Include how you added more knowledge in the final chapter.
  • Add recent references during submission by including more recent work done, which did not previously exist in your initial literature review.
  • More resources on critical thinking here:

Reading research articles

Reading research articles can be a daunting task for new students. Even after reading many articles over the last few years, I still take time to read, understand and critically evaluate research articles (Takes double the time for theoretical ones, since I’m from a technical background). I’m no expert on this topic (or any topic for that matter :p), but I thought this post could be useful for fellow students who toil with research papers just like me. The post is going to be a combination of few tips from my own experience plus a useful course I attended at UTS by Dr. Terry Royce (Reading & writing for your Literature Review: Getting started and what to look for).

glasses-272399_1920

The first thing to keep in mind while reading articles is that it is a time consuming process. So do not get dejected if it takes longer than your allotted time. Not everyone reads in the same pace as you. Give yourself more time, especially if you’re reading a new topic. Your reading skills will definitely improve with experience.

Concentration is key, so take a break and refresh your mind if you’re stuck with an article for very long. How I wish it was as easy as reading a fiction novel for hours with absolute concentration… Sigh!!

Read articles in whichever form that is comfortable for you – either soft or hard copy is your choice. I recently moved from hard copy to soft copy format since it is more convenient to look up my notes anytime and easily portable. I still print important articles and make them ugly with highlights though 🙂 Managing and organising all articles you’ve read/ going to read is another arduous task, for which you probably have to plan early!

Now for the ‘real strategies’ for reading:

  • Read widely and extensively. When you get a fuzzy boundary sense after extensive reading (that the article doesn’t add anything new), that’s when you stop. PhD students might want to read over 300 articles before writing their thesis 😮
  • Learn ‘purposeful focused reading’ – you don’t necessarily have to read a whole book if you only need a chapter of it. Similarly, you can only read what you need in an article.
  • Employ these reading strategies:
    • Reviewing (looking at title, keywords and flipping through)
    • Skimming (for an overview)
    • Scanning (locating specific information or ideas)
    • Reading analytically (text structure, categories, hierarchies)
    • Close reading (observing details)
    • Reading critically (connecting what you read to what you know)
  • Identify the key features and the research arguments from the paper.
  • Look out for the important and relevant details from different entry points:
    • Abstract – What is the issue/project/question? What are the methods/argument/point of view? What are the results and implications?
    • Introduction – What is the topic area? What are the definitions, issue parameters and stages?
    • Conclusion – What are the general areas and specifics covered? Is the focus from the introduction reiterated? What are the implications?
    • Thesis – What is the claim made?
    • Evidence – Is the evidence presented, interpreted and connected to the claim?
    • Rhetorical Staging – How is the article rhetorically staged? (You should be able to draw a diagram to build and reinforce the points stated, if the article is well written)
    • Alternative views – Are there other points of view or counter arguments?
  • Think critically by learning to hear your own voice amidst the authors’ voices in the paper, and be sceptic (not cynic) – This critical thinking skill is another topic by itself!
  • Get started, summarise relevant points, assess the claims around your research and reflect to make critical judgements.

Happy Reading 😀

Hello again!

After a long inactive period of about two years, I’m finally bringing my blog back to life. Yayy!!!
I will be blogging on topics related to learning analytics, text and writing analytics, learning technologies, educational research and probably more general research topics that are useful for fellow PhD students.

Note: I’ve tried to import my previous blog on the Dalmooc course, but there are a few errors in importing the pictures. You can still view the original posts at http://shibanila.blogspot.com.au/

hello

My reflections on DALMOOC

It’s been a great experience working with the Data, Analytics and Learning MOOC (DALMOOC) from EdX at https://www.edx.org/course/data-analytics-learning-utarlingtonx-link5-10x#.VJO6xF4AA. I’m so glad  my boss found it and encouraged me to take it (Although I started only in Week 5 of the 9-Week course). It was not easy, nevertheless, it was a very rewarding experience. I would think an estimated 5 hours/week would not be sufficient to gain all the competencies, especially as the topics become tougher in the later weeks. 
I liked the overall design of the course where the contents progress smoothly, starting from a general introduction of learning analytics, getting started with data analysis to deeper topics like social network analysis, prediction modeling and text mining. Sadly, I jumped back and forth not following the flow, since I started late. But that’s the best part, because the course is designed to be flexible, as in, I can directly access topics which interest me the most, without a need to follow the order. I also liked the fact there were ways to create artifacts for future use and easy sharing, like my blog which I created exclusively for this course!
The contents were good and aptly selected. There are deeper stuff in each of these topics, but this course provides the basic foundation, based on which we can explore further. I found Weeks 5 & 6 to be content-rich which was necessary to understand many concepts in Weeks 7 & 8. Activities with specific points allotted in Weeks 5&6 were good, I wish there were more in other topics to test our understanding (but better organized and with some tips).The voices from the field about different research taking place in this field are very useful bonus content.

The ways to interact were well-planned and executed. The discussion forum was so useful and the students were eager to jump in to answer questions and help others. Connecting with twitter helped us to connect to instructors and other course mates with ease.  I din’t have luck to go into the collaborative chat though. Along with the different options of connecting with social networks like Facebook and twitter (especially in Prosolo), I personally prefer to add in LinkedIn,Google groups as well.
The instructors asked us to reflect more than to see the different activities only as homework, and not to challenge ourselves to do everything, unfortunately, both of which I was doing at times 🙁 This is what happens when we skip some initial weeks where important instructions are given. Anyways, considering the fact that this is the first MOOC I’ve ever taken, I give credit to myself for doing reasonable well. Yay 🙂

In terms of improvements that I would want to see in future courses, most of them are related to accessibility. Most available options, though explained upfront during orientation could be better organized. For example, the different contents in EdX that were in tabbed format were not much noticeable. I used to go to Prosolo every time to see all the contents since I thought EdX didn’t have them all (how stupid of me, lol). Another big difficulty when I first started using EdX is to identify which video I should see first in a chapter. I would often see one, and go to the next as given in alphabetical order and then realize that I should have watched the second one first. It would be good if the course contents are numbered and ordered that way!

The option to download handouts was not given in all weeks, so giving that would help. Making the bazaar assignments more accessible would also help students. Rather than linking to en external assignment bank (which didn’t have all the weeks’ assignments), it would be better if it’s integrated within EdX or Prosolo. I discovered late that Quickhelper has two different options like Question and Discussion (A tip while hovering could help, maybe). And I didn’t know if I should post under each week’s course or the discussion forum link (I later came to know they both link to the same place). Probably, consider any one integrated place for all discussion? Of course, as our instructors talked about future plans, it would be good to see real-time analysis to check who stands where in the network and whom we should get connected to for better access. 

Those were some suggestions I could think of to overcome the difficulties I faced while taking this course. I think these would be definitely looked after in future courses. For the construction of such a course for first time, it is so well done. I’ve learned so much in so little time (miles to go, yet). Big appreciation to all instructors who put in so much time and effort to bring it together. Thank you so much!

After toiling for days to learn, do assignments and acquire competencies, it’s now time to relax . And don’t forget to connect to peers. Happy holidays everyone!

Follow @ShibaniAntonett








Concept Map

Competency 9.2: Integrate various course concepts through creation of a graphical representation (concept map) of the relationships between prominent course topics.

We’ve had a fruitful time learning about different topics in Learning Analytics. The next task in hand was to create a concept map by finding connections among them. With an assurance from the instructors that there is no right or wrong concept map, I started constructing it using the tool available from http://cmap.ihmc.us/download/

If you are going to do it for the first time just like me, I would recommend you to read about concept maps from few articles (The links given in the course were useful). 

Since the concept maps follow hierarchy, I started from “Learning”, since the impact on learning is what all methods look for, though the steps differ. I noticed that text mining and prediction modeling were connected very well. All these methods were also following the same learning analytics cycle. Our course structure reminds me of an opportunity where all these methods can be implemented to get a bigger picture of our learning using analysis of social media, logs, chats, discussion forums etc. Anyways, here it goes, my concept map integrating the different course concepts. (I know it’s a bit cluttered, but bear with me, coz I’m so much in a getting-ready-for-holiday mood and didn’t feel like cleaning up more!)