Autonomous Educational Testing System Using Unsupervised Feature Learning.

International Journal on Integrating Technology in Education (IJITE) Vol.2, No.3, September 2013
DOI :10.5121/ijite.2013.2304 33
Autonomous Educational Testing System Using
Unsupervised Feature Learning.
Anwaya Aras1
, Shree Ranjani2
, Jannat Talwar3
, Dr. Mangesh Bedekar4
1,2,3,4
Department of CS&IS, BITS Pilani K K Birla Goa Campus,
Zuarinagar, Goa, PIN-403726, India.
Abstract
With the increase of ubiquitous data all over the internet, intelligent classroom systems that integrate
traditional learning techniques with modern e-learning tools have become quite popular and necessary
today. Although a substantial amount of work has been done in the field of e-learning, specifically in
automation of objective question and answer evaluation, personalized learning, adaptive evaluation
systems, the field of qualitative analysis of a student’s subjective paragraph answers remains unexplored to
a large extent.
The traditional board, chalk, talk based classroom scenario involves a teacher setting question papers
based on the concepts taught, checks the answers written by students manually and thus evaluates the
students’ performance. However, setting question papers remains a time consuming process with the
teacher having to bother about question quality, level of difficulty and redundancy. In addition the process
of manually correcting students’ answers is a cumbersome and tedious task especially where the class size
is large.
In this paper, we put forth the design, analysis and implementation details along with some experimental
outputs to build a system that integrates all the above mentioned tasks with minimal teacher involvement
that not only automates the traditional classroom scenario but also overcomes its inherent shortcomings
and fallacies.
Keywords:
Machine learning, Natural language processing, Intelligent tutoring, Pedagogy Enhancement E-learning
techniques, Document Object Model Parser, Information Retrieval, Web data mining.
1. Introduction
Our system broadly handles two major tasks: generating questions and evaluation of subjective
answers. The system extracts questions from a webpage on the Internet using web data mining
techniques based on the topic chosen by the teacher. Since the internet is a varied and colossal
repository of information, it is necessary to categorize questions based on their relevance to the
topic chosen by the teacher. This is done using classification algorithms under machine learning
and the teacher is only required to provide the initial training set. The question database is further
updated with an additional entry for difficulty level per question. The students can now take the
test as per their convenience to test their understanding of the subject.

34
Once the student starts the test, the system simultaneously evaluates his subjective answers in real
time. Based on the student’s performance, the system increases or decreases the difficulty level of
his questions. Subjective answer evaluation is done using natural language processing techniques
where the system determines the percentage correctness of a students’ answer by comparing it
with a standard answer from the database. This method of checking ensures an unbiased and
efficient evaluation of textual paragraphed answers.
Our system effectively analyses the level of comprehension of a particular topic by a student.
Automation of the traditional classroom scenario is accomplished by making optimal usage of
web resources and thus we have a robust, fully functional intelligent classroom scenario in place.
2. State of the Art
In structuring a quiz it is important to work with a theoretical framework of learning. Assessment
processes are now in the limelight, with increasing emphasis placed not on testing discrete skills
or on measuring what people know, but on fostering learning and transfer of knowledge. The
traditional approach to assessment is largely a form of objective testing which tends to value
students' capacity to memorize facts and then recall them during a test situation. Typically,
objective quiz questions are considered limited in their capacity to assess cognitive skills of a
student. These types of questions promote guessing and do not reveal the level of thinking of the
student. They often fail to provide a learning experience for students as they only deal with
factual recall.
Subjective questions can be used to test students’ ability to synthesize and apply concepts. This
paper introduces a system for subjective quiz generation and automatic correction and analysis of
answers. Using this system, a teacher can make a deeper evaluation on the students' performance,
instead of only checking his/her marks. In addition, this system fosters self-regulated learning as
students can give the tests at a time and place convenient to them.
I. Proposed Architecture:
Fig1. Methodology-Teacher Interface

35
Fig2. Methodology-Student Interface
III.A. Question Generation
Generating subjective questions is the basic requirement of this system. This task has been
accomplished using web data mining. Web mining adopts data mining techniques to
automatically discover and retrieve information from web documents. A web data extraction
system usually interacts with a web source and extracts data stored in it: for instance, if the source
is a HTML web page, the extracted information could consist of elements in the page as well as
the full-text of the page itself. Eventually, extracted data might be post-processed, converted in
the most convenient structured format and stored for further usage [1].
Web data extraction allows to efficiently collect this information with a limited human effort. The
role of the teacher in subjective question generation is limited to only providing the url of the
webpage which contains the required questions and answers. All the remaining work is handled
by the web data extraction system. The design and implementation of this system has been
considered from different perspectives and it leverages on scientific tools coming from various
disciplines like Machine Learning, Logic and Natural Language Processing. As this system
involves web data mining, many factors were taken into account; some of which were
independent of the specific application domain in which we planned to perform web data mining.
Other factors, instead, heavily depended on the particular features of our application domain: as a
consequence, some technological solutions were not suitable for this system. Some approaches
focused on static HTML web pages and used the tags composing a page along with their
hierarchical organization to extract information.
i. Related Work.
There is a large body of related work in data extraction and information retrieval that attempts to
solve similar problems using various other techniques. Although much research has been done on
data extraction, it is still a relatively new field. Laender [2], presented a survey that covers a
rigorous taxonomy to classify and analyze web data mining. They introduced a set of criteria and
a qualitative analysis of various Web Mining tools. Kushmerick [3] tracked a profile of finite-
state approaches to the Web Data Mining problem. Web Data Mining techniques derived from
Natural Language Processing and Hidden Markov Models have also been discussed in the past.
[3]

36
ii.Our approach
The url of the webpage and the specific topic are taken as input from the teacher. The questions
along with their respective answers are then scraped from the url and shown to the teacher for
confirmation. Here we were faced with a choice. We could either use Regular expression
matching or HTML DOM parsing[4] to extract the questions. Although regular expression
matching was an easier option, we decided to go with the latter since it is less processor intensive
and has a lesser chance of failing if the layout of the page changes slightly. The representation of
a Web page by using a labeled ordered rooted tree is usually referred as DOM (Document Object
Model). The general idea behind the Document Object Model is that HTML Web pages are
represented by means of plain text, which contains HTML tags, i.e., particular keywords defined
in the mark-up language that can be interpreted by the browser to represent the elements specific
of a Web page (e.g., hyper-links, buttons, images and so forth). HTML tags may be nested one
into another, forming a hierarchical structure. This hierarchy is captured in the DOM by the
document tree, whose nodes represent HTML tags [5]. By parsing the webpage into a DOM tree,
more control can be achieved while extracting content. This concept was used to implement the
extraction of questions by this system. After confirmation, the question-and-answer sets are added
to the database of questions already present. If the topic is new, it is also added to the table of
topics in the database.
III.B.Text categorization
As the data obtained after extraction from internet may be varied and not be typically relevant,
categorizing into topics is essential. The goal of text categorisation in this context is categorising
the question according to the topic chosen.Using machine learning the objective is to learn
classifiers from examples which do the categorisation automatically.The categorisation in this
case boils down to binary classification as to whether the question belongs to the topic or not.
Traditionally, 5 major approaches have been used to solve this problem of text categorisation
namely Naive Bayes algorithm,Support vector machines,Artificial neural networks,k-Nearest
neighbour classifier,C4.5 decision tree learner.SVMs are the best conventional method in
comparison with others excluding Artificial Neural Networks.[6]Support vector machines with its
ability to handle high dimensional input space,sparse instances of data,few irrelevant features
scores high over the remaining. Previous works also suggest that SVMs deliver state-of –the –art
classification performance. However,success on benchmarks is a brittle justificatiom for a
learning algorithm and gives only limited insight.
On the other hand,with respect to text categorisation, Artificial neural networks has fared
comparable to SVMs.The performance of ANNs is statistically comparable to SVMs even with a
reduced data size. ANNs have been shown as one of the best text classifiers[7]. Hence we reduced
the multitude of options available for classification to ANN and SVMs.
i. Our approach
Our initial implementation used both ANNs and SVMs. But we finally used an algorithm similar
to back propagation ANN .This is due to fact that SVMs are not suitable with instances where
there are many well populated categories. In this system as the teacher is providing a
comparatively relevant site for question extraction there is very less chance of obtaining few well
populated categories.

37
ii. Theoretical model
In back propagation there are 2 phases in its learning cycle, one to propagate the input pattern
through the network and the other to adapt the output, by changing the weights in the network.
The training procedure of a back propagation is iterative, with the weights adjusted after every
testing phase. The underlying principle was mainly back propagation but was slightly modified
while implementing.
iii. Designed Algorithm
 Word extraction:
Any question( sequence of characters) should be converted to words or tokens for the purpose
of feature identification.This process is called as word extraction techniques depends on the
language used. We have restricted to English in our system and hence word demarcation was
done with the help of spaces and full stops.
 Stop word removal
The process of removing the set of non-content bearing functional words like
(what,where,is,was,etc) from the set of words produced after word extraction is called stop
words removal.As these words are not unique to any particular topic, removal of these words
will increase the accuracy. An exhaustive list of 400 odd stop words was used and removed
from the words obtained after extraction. In text categorisation tasks it is desirable to combine
morphological variants of the same words into one canonical form. However usage of
stemming has its own limitations which can be elucidated with an example.Words like
generous,generate,generates all reduce to gener giving absurd results.Results obtained
without stemming were better comparatively in this system.
 Giving weights to terms and Training
In the training phase the teacher goes through the questions and assigns one of the values 1 or
-1 depending on whether the question belongs to the topic or not.After this assignment
terms/tokens are given positive or negative weights based on whether the question that
contains the word has been assigned 1 /-1 or the token is identified as essential by the
teacher.The teacher is given the privilege to add words that he/she feels should necessarily
belong to that particular topic and assign their corresponding weights.Provision for
modification of word-weights /deletion of words by the teacher has added dynamism as well
as accuracy as there is always a possibility that initial training questions may not contain all
typical words of that topic.Hence a word-weight is generated at the end of the training phase
which is analysed and a threshold is calculated by plotting all word-weights and the highest
local minima was chosen as this will not rule out words that are important but appeared less
frequently in the question data set.
 Testing phase
Training is required only when the teacher introduces a topic to the students. Words identified
as having weights above the threshold in the training phase are saved to be used in later
stages.
As testing proceeds weights of words already present in the database may get modified
depending on their frequency in the subsequent sets of questions. They may also get modified
if the teacher realizes that some typical words that should necessarily belong to the topic are

38
not present in the database. With the help of word-weight matrix weights for each question is
calculated and those with weights above question threshold are classified as positive.The
initial question threshold is calculated using word-weights above threshold in the training
phase ,even this threshold changes with time as word-weights get modified.
III. C. Summarisation and Evaluation of Answers
Efficient summarization of answers, their automated evaluation and qualitative analysis bring us
to the last part of this project. Work on summarization of large amounts of data has been
effectively done and substantial results have been achieved(Interactive Multimedia Summaries of
Evaluative Text ,Giuseppe Carenini , et al.,2006) .However summarization of brief answers
written by students, adapted to a particular style of writing and a characteristic concept flow,
requires a different approach. The idea we used behind summarization derives inspiration from
the way a naive school teacher evaluates copies of her students' examination answer sheets. Just
as the teacher gazes through a certain specific set of important words, the machine is trained to
iterate through student answers and extract the important set of words. Relevant words from each
answer are extracted and every answer is thus converted into a word matrix.
i. Related work
The algorithm we have used for summarization has derived inspiration from basic NLP
techniques like Name Entity Recognition(NER), Part of Speech tagging(POS tagging) and uses
concepts of ranked information retrieval, parsing and question answer techniques. However, we
have modified the standard summarization algorithms to incorporate the fact that we deal with
paragraphed answers that do not exceed a certain maximum limit.
Initially, the answer is fragmented and divided into multiple tokens. Then a Part Of Speech tagger
is then run on each sentence and the words are tagged accordingly. However, this tagging is not
accurate enough and hence we have used a novel technique for tagging based on a naturally
growing resource -Wikitionary (Wiki-ly Supervised Part-of-Speech Tagging ,Shen Li et al.,2012).
Once this tagging is complete, all nouns, adjectives and verbs are put into the summarization file.
If the student bolds/underlines any specific word,the word is put into the word matrix without
taking its part of speech into consideration.The summarization of paragraph answers is thus
complete. The case where student answers in form of bulleted points is handled slightly
differently. In case the bulleted points consist of a few words, all the words are included in the
summarized file along with the point number.In case the bulleted points consist of a few
sentences, the sentences are summarized and referenced with respect to the point.Thus, the order
of words and hence the flow of ideas is preserved and we get a lexically abridged version of
student answers.
ii. Our approach
The following are all the steps that encompass the entire algorithm that runs behind our
summarization. Each of the techniques described below comprise of a separate module running at
the back end of our summarization module.
 Stemming
We use stemming to narrow our overall word matrix so as to help with the lack of similar words
per answer. We have used the standard Porter's stemming algorithm for the purpose.Overall, we

39
see mixed results for stemming.While stemming shows improvement it also negatively affects the
overall accuracy when combined with other aspects of student answers.
For eg: Operating Systems-> Operat
Operations Systems->Operat.
 Stop Word Removal
As in much text, that there were many common, short function words, often prepositional terms
and other grammatical syntax fillers that were found specially in descriptive answers. We have
not yet attempted to build a domain-specific we used a standard set of Porter stem words.
Ex: the, is, at, who, which, on
Stop words removal turned out to drastically reduce the length of passage and descriptive answers
thereby giving huge accuracy boost.
 Named entity recognition
Entity identification is a very important part of summarization specially because it tells the
system extremely relevant data about the student's answer.Though NER systems are known to be
brittle, our system has been specifically designed for the student answer domain and thus it
rightly extracts the necessary words depending upon the type and setting of the question.
 Punctuation Removal
Removing punctuation was another result of looking at our data and noticing that students have a
very large variance in phrasing and word choice. This is especially true for words that may have
multiple accepted forms, or words with punctuation in them. Also, because we parse the item
descriptors on spaces, any punctuations that are in the phrase are left in, including ellipses,
periods, exclamation points, and others. In addition, words that are concatenated are often used
differently. Punctuation removal was the also an effective feature normalization method used for
summarization.
 Lowercasing
While answering in English, students tend to have the first word capitalized. In addition, different
students will capitalize different words intentionally or otherwise, depending on their intent
interpretation of the word, of choice of capitalizing acronyms. This is generally not a useful
normalization for the system to understand as it deteriorates the performance of the POS tagger.
Ex. President, president, CD, cd, Windows, windows
After application of the above techniques, the system generates a relevant set of words known as
the summarised answer matrix for each answer. For evaluation purposes, the word matrix of
student answers is compared aginst the word matrix of standard answers and depending upon the
number of matches,the student is awarded marks. As the summarization process is robust and full
proof,this comparison ensure maximum efficiency and the machine has to compare only the
necessary and relevant parts of answers thereby reducing processing time and increasing
efficiency.
IV.Implementation Details.
We have worked on a XAMPP PLATFORM using PHP,MySQL,JavaScript and Python as our
primary languages.Web scraping has been used to scrape data (questions and answers) from the
specific url. It involves the process of querying a source url, retrieving the results page and

40
parsing the page to obtain the results. The web page to be analysed is fetched and processed in
order to retrieve the data. Web data is mined using the tree-like DOM structure to analyse and
describe the HTML or XML tags within the web page. Instead of defining one generic function,
specific functions have been created for each website to efficiently process them.
The code for extraction has been written in PHP and JavaScript framework which adds
functionality to the code. On the backend, tasks like downloading a webpage, extracting the
questions from it, intelligently processing using machine learning them to get relevant questions
and finally storing them in a MySQL database for future use are handled by PHP.
Though the machine learning algorithm for this was initially written in JAVA it was later adapted
to PHP for increasing compatibility.Frontend tasks like displaying the questions and providing the
ability to edit them in place is handled by the JavaScript Library and JQuery. Extensive care has
been taken to ensure cross-browser compatibility.
There is also an option to directly add a specific question and answer pair to the database using
the extension designed specifically for this purpose. If the teacher finds some interesting question
while surfing the net, he can directly add it to the database by selecting the question and answer
text and clicking on the extension icon. There is also an option to edit the question or answer
before adding it to the database.

41
The summarization and student answer evaluation part has been implemented in Python
programming language using NLTK library. Since python contain an extensive library for
language processing, it stood out as the best language for development of robust modules for this
project. We have developed our own POS tagger,tokeniser and hand codded features for the
named entity recognition algorithm based on the dominant aspects of answers written by
students.These modules when run together successfully extract the summary of an answer,
evaluate it based on a standard set of answers , accordingly mark the students and thus access his
performance.
II. Testing and Statistical Results
At the initial stages,as the database had questions of the order of few hundreds,there were not
enough questions for testing.As the essence of machine learning lies on training the machine
using maximum data ,the accuracy of classification increased as the database grew.The accuracy
of classification moved from 63% to 80% as the database size moved from few hundreds of
questions to thousands.Also,when the training and testing set ratio increased the accuracy further
improved.When this ratio changed from 3:2 to 4:1 efficiency further improved from 81% to 89%.

42
Extracting data from small document was a challenge as there was limited literature available for
us. Hence, we have done extensive evaluation of the algorithm we implemented for the same. We
have used the ROUGE criterion for evaluation. We started with ROUGE-1 .Though the rouge
scores were impressive, this did not take care of the order of words in a sentence. Hence we
resortd to a more accurate ROUGE metric-the ROUGE-L metric.
To see of there is an improvement if we increase the number of references , we also tried with 3
references for ROUGE. However, we discovered that unlike large data analysis, there was not
much improvement seen in Rouge scores as the number of references increased.This observation
prevails particularly for sentence level analysis.Hence, for the final version, we have used only
one reference from the database. A brief table describing our results for summarisation is as
follows:
10 Answers
1 Ref
10 Answers
3 Ref
100 Answers
1 Ref
100 Answers
3 Ref
1000 Answers
1 Ref
1000 Answers
3 Ref
ROUGE-1 0.91 0.92 0.95 0.95 0.97 0.97
ROUGE-L 0.76 0.78 0.82 0.85 0.88 0.89
VII. Conclusion and Future Work:
Our approach, working with the Document Object Model (DOM) tree as opposed to regular
expression matching, enables us to perform data extraction, identifying the original data and
summarizing it. The techniques that we have employed, though simple, are quite effective.
Currently, though we are handling many webpages, we plan to make suitable changes in our
system so that any webpage can be used for data extraction in future. In future, we plan on

43
making our extension browser compatible and more user friendly so that it can work in any
interface in which the system is installed.
Classification was currently done based on the words and weights in questions, but words that
meant the same should be allocated same weights by the system and then used for classification.
This can increase the credibility of the system enormously.
One main difficulty was encountered in summarisation. For answers that are extensively
descriptive, students used different forms of the same word i.e. synonyms or related words. This
was where the efficiency of the system declined as it failed to understand the similarity.
Accessing the thesaurus corpora was possible but accessing it for each answer took vast amounts
of time and was also unnecessary in most of the cases. Thus, there is trade-off between system
efficiency and judicious resource management. Currently, though this issue has been handled by
letting the teacher make a choice about entailing the thesaurus corpora, we plan to make modest
changes in the software to handle this issue at the implementation level in future.
In conclusion,we have addressed the challenging problem of developing a fully automated
intelligent testroom scenario with minimal human intervention successfully developed a system
to support our claim. Generation,summarization and automated evaluation of student answers is
achieved and we thus have a robust, fully functional intelligent classroom scenario in place.We
feel this takes us one step closer to realising the Artificial Intelligence dream of building fully
functional autonomous bots some day.
References:
[1] Irmak U and Suel T. 2006. Interactive wrapper genera-tion with minimal user effort. In Proceedings
of the 15th World Wide Web. Scotland, May 23-26, 2006. PP553-563.
[2] Laender, A. H. F., Ribeiro-Neto, B. A., da Silva, A. S., and Teixeira, J. S.2002. A brief survey of web
data extraction tools. SIGMOD Rec. 31,2, 84
[3] Kushmerick, N.2002. Finite-state approaches to web information extraction. Proc. of 3rd Summer
Convention on Information Extraction,
[4] A. F. R. Rahman, H. Alam and R. Hartono. “Content Extraction from HTML Documents”. In 1st Int.
Workshop on Web Document Analysis (WDA2001), 2001.
[5] A. F. R. Rahman, H. Alam and R. Hartono. “Understanding the Flow of Content in Summarizing
HTML Documents”. In Int. Workshop on Document Layout Interpretation and its Applications,
DLIA01, Sep., 2001.
[6] Text Categorisation with Support Vector Machines:Learning with many relevant feactures –Thorsten
Joachims
[7] Waleed Zaghloul, Sang M. Lee, Silvana Trimi, (2009) "Text classification: neural networks vs
support vector machines", Industrial Management & Data Systems, Vol. 109 Iss: 5, pp.708 - 717
[8] Document Classification with Unsupervised Artificial Neural Networks-Dieter Merkl and Andreas
Rauber
[9] Support Vector Machines for Text CategorizationA. Basu, C. Watters, and M. ShepherdProceedings
of the 36th Hawaii International Conference on System Sciences – 2003
[10] An Extensive Empirical Study of Feature Selection Metrics for Text Classification George
Forman,Intelligent Enterprise Technology Laboratory
[11] The Simple Bayesian Classifier as a Classification Algorithm -Leon Versteegen
[12] Demian Antony D'Mello,(2012), “Functional Semantics Aware Broker Based Architecture for E-
Learning Web Services”, International Journal on Integrating technology in Education,, ISSN 2320-
1886,Vol1,Issue1.

Autonomous Educational Testing System Using Unsupervised Feature Learning.

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