performance analysis for blended moocs on iitbombayx...limitations of pure classroom based or pure...

Performance Analysis for BlendedMOOCs on IITBombayX

Submitted in partial fulfillment of the requirements

of the degree of

Master of Technology

by

Rahul Dev Parashar

(Roll No. 13305R006)

Supervisor:

Prof. Deepak B Phatak

Department of Computer Science and Engineering

Indian Institute of Technology Bombay

2015

Abstract

Multiple institutes are partnering with IIT Bombay to offer blended MOOCs. Students

will study the online course on IITBombayX, and will also study the same course normally

in their institute. Final grade will be based on the composite performance of students,

in the online assessment, and in their regular tests/exams at the institute. In blended

model, it is important to understand the learning of each student and their performances.

Considering large number of students in each MOOC, it is not possible to do manual

analysis. So, an automated system is needed to do this analysis. The objective is to

design and implement a system to facilitate performance analysis of students of different

participating institutions. Using this system, a teacher from such an institute will be able

to (i) compare performances of local students with that of other students, (ii) compare

performances of students in local and online assessments, and (iii) view the event log

analytics to compare learning habits of students.

i

Contents

Abstract ii

List of Figures iv

1 Introduction 1

1.1 MOOCs (Massive Open Online Courses) . . . . . . . . . . . . . . . . . . . 1

1.1.1 Advantages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.1.2 Disadvantages and Challenges . . . . . . . . . . . . . . . . . . . . . 2

1.2 Blended Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2.1 Benefits over MOOCs . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2.2 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.3 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2 Literature Survey 4

2.1 IITBombayX Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2.2 Various Data Modules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.2.1 Student Information and Progress Data . . . . . . . . . . . . . . . . 5

2.2.2 Discussion Forum Data . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.2.3 Tracking Logs Data . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.3 Events in Tacking Logs (Student Engagement) . . . . . . . . . . . . . . . . 5

2.4 Student Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

3 Proposed Approach and Prototype 8

3.1 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

3.2 Proposed Method and Prototype . . . . . . . . . . . . . . . . . . . . . . . 10

ii

List of Figures CONTENTS

3.2.1 System Architecture: . . . . . . . . . . . . . . . . . . . . . . . . . . 10

3.2.2 Available Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3.2.3 Data Cleaning (Prototype) . . . . . . . . . . . . . . . . . . . . . . . 13

3.2.4 Data Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

4 Obserations 17

4.1 Some Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

5 Future Work and Conclusion 19

5.1 Stage-2 Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

5.2 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

iii

List of Figures

2.1 Sample log for video interaction event . . . . . . . . . . . . . . . . . . . . . 6

3.1 Architecture of data analytic system . . . . . . . . . . . . . . . . . . . . . 10

3.2 Various data modules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3.3 Typical analytic report (Source: Open edX Insights) . . . . . . . . . . . . 16

4.1 Invalidated JSON objects . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

iv

Chapter 1

Introduction

In section 1.1 and 1.2, working of MOOCs and Blended learning is discussed, and in

section 1.3, problem statement is discussed.

1.1 MOOCs (Massive Open Online Courses)

Massive Open Online Courses (MOOCs) are model for delivering learning content online

to any person who wants to take a course, with flexibility to view and access content

anytime, anywhere. In addition to traditional course materials such as lectures, reading

materials, exams, and class discussions, MOOC provides discussion forum to interact with

instructors, teaching assistants, and other participants. Since a few years, it has emerged

as a popular mode in distant learning. They have some signature characteristics that

include: lectures formatted as short videos combined with formative quizzes; automated

assessment and/or peer and self–assessment, and an online forum for peer support and

discussion.

1.1.1 Advantages

MOOCs are delivered by top-tier institutions and not to just a few hundred students in a

lecture hall on campuses, but free via the Internet to thousands or even millions around

the world. Typically, students watch short video lectures and complete assignments that

are graded either by machines or by other participants of the course. That way a lone

1

Chapter 1. Introduction 2

professor can support a large class with the help of Teaching Assistants (TAs).

1.1.2 Disadvantages and Challenges

One of the biggest challenge is, that how can you effectively teach thousands of students

simultaneously, where each student’s learning style and capabilities are different. As the

class size is large, and if the teacher is not aware of learning style of students, then the

effectiveness of learning can be low.

1.2 Blended Learning

Blended learning is an education program where a student learns in through MOOC, as

well as through their regular course curriculum in their institute.

1.2.1 Benefits over MOOCs

Blended model is more effective than alone either regular class based learning or MOOC.

Learning only through a traditional class may be hindered by the ability of teacher to teach

the subject; where as, learning through MOOCS, depends highly on the motivation and

self learning of participants. Blended learning overcomes these challenges by providing

collaborative learning experience. In blended learning students with special talents or

interests outside of the available curriculum can use educational technology to advance

their skills. Also students which have difficulty in learning the material can seek help

from either class teacher or discussion forum. So this collaborative model overcomes the

limitations of pure classroom based or pure MOOC learning.

1.2.2 Challenges

For best use of blended model, MOOC and class room learning must be in sync. Matching

the course content of a MOOC can be challenging in blended model, because faculty from

various institutions might have different syllabus, either as per their college curriculum or

their own interest. Also, the lecture recording technologies can result in students lagging

2

Chapter 1. Introduction 3

behind on the course material. Students may also watch several weeks’ worth of videos

in one sitting.

1.3 Problem Statement

As it is not possible to keep track of every student and their learning behavior, an auto-

mated system is needed which can create a time line for each student, according to their

engagement with the course. This analysis can help in self regulated learning environment

of MOOCs. Further, Blended MOOC require students’ performance in classroom as well

as in MOOC environment, and how it can best be used. The performance analysis of stu-

dents can help in achieving the better outcomes. In this report, performance analysis is

done on students from partnering institutes, offering blended MOOC with IITBombayX.

Objective: The purpose is to design and implement a system to permit performance

analysis of students from different participating institutions. Using this system, a teacher

from such an institute will be able to:

• Compare performance of local students with that of other students.

• Compare performance of students in local and online assessments.

• View the event logs analytics to compare learning habits of students.

There are certain parameters which can be used for analyzing performance of students.

Some typical characteristics/questions are following.

• How many students are solving questions before going through study material?

This can help in understanding knowledge of student for a particular module.

• What are grades of students for a particular class in comparison with other students?

• Is there any relation between performance on MOOC and classroom learning(provided

teacher has submitted classroom grades on IITBombayX)?

If there is much difference in performance of a student in one of the mode, then

teacher can take corrective action.

• Any other feedback that might help for better learning?

3

Chapter 2

Literature Survey

2.1 IITBombayX Architecture

Open edX is a web-based platform for creating, delivering, and analyzing online courses.

IITBombayX uses architecture of open edX. IITBombayX also provides support for Blended

Learning. Separate authentication process is used in blended model as a wrapper to open

edX.

OpenEdX Components:

• CMS (Content management system): This component allows for the authoring of

tools. A Django application uses MongoDB(NoSQL) for content management.

• LMS (Learning Management System): The part of OpenEdX that students interact

with. It displays content, runs quizzes and interactive applications. It’s subcompo-

nents are Wiki, Discussion Forum, etc.

• Event Tracking: Track events for any interaction with the system. Capture and store

events with nested data structures in order to truly take advantage of schemaless

data storage systems. These event logs are stored as JSON objects.

• Open edX Insights and Analytics: Insights is a development version of a Python,

Mongo, and Django framework for creating simple, pluggable analytics based on

streaming events. This does not include the analysis of every event from logs.

4

Chapter 2. Literature Survey 5

2.2 Various Data Modules

IITBombayX data is stored in various data modules. For the ease of storage and interac-

tion with data, various designs are used. These are the various data models which stores

related data.

2.2.1 Student Information and Progress Data

General information about students and their progress is stored in MySQL database. This

can be termed as summary information about students. Open edX Insight makes use of

this data to give simple analytics. Information about assignments, quizzes, and exams is

stored here.

2.2.2 Discussion Forum Data

IITBombayX discussion forum data is stored as collections of JSON documents in a

MongoDB database. It gives information about students interaction with other students.

Comment threads are used to analyze this data.

2.2.3 Tracking Logs Data

Whenever a student interacts with the course, every action by the student is stored in

logs, classified based on event type. For example, whenever a student clicks on some video

to watch or to pause, these events are stored in logs with the adequate information to

analyze it. Events are emitted by the server, the browser, or the mobile device to capture

information about interactions with the courseware and the Instructor Dashboard in the

LMS, and are stored in JSON documents.

2.3 Events in Tacking Logs (Student Engagement)

Tracking logs can be classified based on event type for which they are generated [? ].

Events comprise of fields which are common to all events, fields related to students activity,

and fields related to course team activity. Here is a sample log for video event:

5


Figure 2.1: Sample log for video interaction event

These logs can be analyzed by checking the events they are emitted from. Some events

and commons fields are detailed below.

• Common Fields: Fields that are common to the schema definitions of all logs.

– Context: It contains course id, org id, path(URL that generated the event),

user id fields.

– Event: This field provides information for the event this log is created.

– Event Source: This field is used to identify the application that was used from

browser or mobile device.

– Event Type: This field provides information about for whom this event is

created. It can be a student or course team member.

– Page: URL of the page, the user was visiting when the event was emitted.

– Time: Gives the UTC time at which the event was emitted.

– UserName: The username of the user who caused the event to be emitted.

• Student Events

– Enrollment Event: Activities like activation, deactivation of account.

– Navigational Events: Events like page close, goto position, and jump to discussion

are found.

6


– Video Interaction Events: It consist of events like hide transcript, load video,

pause video, play video, seek video, show transcript, speed change video, stop video,

etc.

– Textbook Interaction Events: Consists of events for interaction with pdf and

other text material provided.

– Problem Interaction Events: Interaction with problems in quizzes and exams

are problem interaction events. Some typical events are problem check, prob-

lem graded, problem save, problem show, save problem success, show answer,

etc.

– Discussion Forum Events: This event is generated when a comment is created,

a response is given, or a new thread is created in discussion forums.

• Course Team Events: It consists of events which are emitted, when a teacher or

admin interacts with the system.

2.4 Student Performance

Performance of students can be measured from their response to quizzes, exams, etc.

There are 2 kinds of content from which performance can be analyzed.

• Graded Content

Graded content contributes toward final score of a student. Overall score of a student

is calculated by taking given weightage of each quizzes, assignment, exams, etc.

• Ungraded Content

Ungraded content do not contribute toward final score of a student. This content

can be used to understand learning ability of a student and improvement in learning

for that course.

7

Chapter 3

Proposed Approach and Prototype

3.1 Problem Statement

To understand the learning behavior of students, it is necessary that their performance in

course should be analyzed properly. As MOOCs are offered to students in large numbers,

it is not possible for a teacher to keep manual check on this. It is also noted that learning

style of each student is different. For that purpose, they must be taught in different

manner. Some student might catch concept really fast and they might want to improve

their learning by solving challenging problems. On the other hand, some students may

find it difficult to learn even the provided material. So for them other resources must be

suggested.

In blended model, the teacher who is providing classroom learning might want to know

that how online learning is helping his students and what is their performance for online

course. This analysis can give him insight about understanding learning behavior of his

students.

To cater the above need, it is essential that performance analysis of students is done

and provided to teachers for both, online and classroom course. IITBombayX uses open

edX Insights to monitor activities for a course. This model provides basic information

about course progress, students responses to quizzes and assignments, and other details

about students. Open edX Insights do not consider all the events that are being generated

in log files. In addition, there are other fields which are not classified under any event.

8

Chapter 3. Proposed Approach and Prototype 9

We have tried to classify them on the basis of their source from which they are emitted

by identifying their properties. Such fields and their possible event source are shown in

Chapter 4.

There are various components for which Open edX Insights generate reports. They

are:

• Course Enrollment

This category gives information about students enrollment, demographics, geogra-

phy etc.

• Student Engagement

This category gives information about the quizzes students have answered, which

choices were selected, their assignment submission status, and interaction with the

videos. Only general information about videos is used for example how long they

have watched and which section was watched again.

• Student Performance

Students’ answers are recorded for graded and non graded quizzes and assignments.

Based on this, various reports are generated. For example how many students have

answered it correctly and so on.

But the Open edX Insight doesn’t provide complete analysis of students’ performance

and their timeline about their interaction with the system. It is essential to show the

learning style of students. For example, which student watches a video first and then

attempts a quiz and vice versa. So, an automated system is needed which can create

a time line for each student according to their engagement with the course, and also to

analyze their performances.

As IITBombayX provide blended courses, it is essential that proper wrappers are

provided over this system to give these reports to their classroom faculty. In addition

to that, for better analyzing performance of their students, comparison among various

students can done with both blended and non blended course students.

In stage I, cleaning and preprocessing of tracking logs is done. This data is stored in

MySQL tables for purpose of prototyping. It has been explained in previous section that

9


for purpose of proper analysis, preprocessing of logs is required. Once we get the data in

proper place, we can start doing performance analysis based on particular characteristics.

This work will be done in stage 2.

3.2 Proposed Method and Prototype

3.2.1 System Architecture:

The architecture diagram for model used in data analytics is shown below.

Figure 3.1: Architecture of data analytic system

The above diagram explains how analysis will be done. Various steps are explained

below in detail.

• Data: As explained in section 2.2, data is present in various modules. Out of these,

10


tracking logs are not structured. So these are preprocessed and cleaned.

• ETL: These tracking logs are cleaned and preprocessed using a JAVA program

based on particular event type.

• Storage: This data will then be moved to MySQL tables. From here, this can be

used for analytics.

• Data Analytics for non blended MOOCs: Now, the available data can be used

to analyze students performance.

• Data Analytics for blended MOOCs: The analysis results for non blended

MOOCs can be wrapped using authentication of blended model. After filtering these

results, reports can be shown to faculty, students, etc., on web based platform.

3.2.2 Available Data

To analyze performance of students, tracking logs provide lot of useful information. But

the format of tracking log is semi-structured. To understand the pattern and classify these

logs based on their events, it is necessary to preprocess and clean them. Once we classify

them based on the events, it becomes easier to analyze this data. Steps followed in the

method are:

1. Identify various data modules which can be brought to use in performance analysis.

2. Clean the data

3. Analyze the data and generate reports for students’ performance.

As detailed earlier, there are various data modules which holds IITBombayX data. For

purpose of performance analysis, we are considering the following data modules. These

data modules are stored in different ways. The uses of each module and it’s storage is

explained in detail below. This is also shown in Figure 3.2.

1. Student Info and Progress Data

This section explains how stateful data for students is stored internally. It contains

general information about students, their name, username, email id, geographical

11


Figure 3.2: Various data modules

details etc. It also stores progress of students in course. Data for students is pre-

sented in these categories:

• User Data: Basic information about the user.

• Courseware Progress Data: It stores information about what material a student

has covered and what were the responses to various modules.

• Certificate Data: It contains final grade, status of certificate, etc.

This data is stored in MySQL tables. Open edX Insights uses this data to produce

various reports. For example how many students are registered in course etc. The

size of the data is less than the tracking log data, and can be useful for generating

reports in less time. This will be used for student performance analysis as required.

A typical report produced by Open edX Insights is shown in Figure 3.3.

2. Course Content Data

Course content data can be used to get information about course modules. We can

check how many quizzes/exams are conducted in course or module wise and other

related information. Information about videos, quizzes, assignments, etc. are stored

in JSON files.

12


3. Discussion Forum Data

IITBombayX discussion forum data is stored as a collection of JSON documents in

MongoDB database. The primary collection that holds all of the discussion posts

written by users is “contents”. Comment and comment threads are created for

discussions. In addition to these collections, events are also emitted to track specific

user activities and is stored in tracking logs. These will be explained in next section.

Wiki data is also stored in SQL files. There are 2 files, one file gives information

about articles added on wiki, while the other contains modifications made to articles

on wiki. This data won’t be used for performance analysis.

4. Tracking Logs

Tracking logs store every activity or interaction with the system. These logs can

be classified based on various events. These events are already discussed in section

2.3. There are various approaches used to understand and use this data for analysis.

Open edX Insights perform basic analysis and gather reports produced from them.

To do this analysis the logs are processed and required information is stored in

MySQL. Then generated MySQL data is used for analysis.

Open edX Insights only captures limited information. Currently, only the video

based events are captured. For proper performance analysis of students, it is nec-

essary to capture other events like, problem interaction event, course interaction

event, etc. For this purpose, this data is cleaned and pre-processed. In the next

section, we explain the importance of data preprocessing and classification based on

events.

3.2.3 Data Cleaning (Prototype)

A JAVA program has been written to break these logs based on the event type. Separate

modules are created for various events. These logs are processed one-by-one, and parsing

is done for JSON objects. After classifying event type, respective object is used to store

that information. Once the processing of that particular log is done, it is stored in a

MySQL database, with each event given a unique id. Summary of each log is written as

user session. Following is the detail for various objects created and schema of MySQL.

13


• Objects Created

To capture the events, various objects are created. Once the event is identified,

data is stored in these objects, which is then stored in SQL tables. Various ob-

jects used are: CourseProblems, CourseQuizzes, CourseVideos, EventCourseInter-

act, EventEnrollment, EventForumInteract, EventProbInteract, EventVideoInter-

act, StudentCourseEnrolment, StudentCourseGrade, UserSession, etc.

These objects have various fields to store required information about that particular

event. After storing data in these objects, these are passed to respective MySQL

tables.

• Tables created

Once the data is stored in objects based on events, they are transfered to MySQL

tables. Some table and their columns are shown in Table 3.1.

Summary of results captured from the prototype are shown in section 3.3.

3.2.4 Data Analytics

The data stored in various modules can be used for analyzing the performance of a stu-

dent. Data generated from logs in actual implementation will be moved to HDFS. Using

this we will be able to get timeline of a student. This can then be used for identifying

learning style of a student.

Open edX Insights, with addition of events which were not considered, can be used for

MOOCs analysis. To use this for blended model, we need to wrap results using authen-

tication mechanism provided for blended model. In this way each faculty will see results

for only their students. In case when we compare performances of students from different

institutes, then this data can also be shown to them. One typical diagram for a weekly

student engagement chart shown in Figure 3.3 displays the number of students who en-

gaged in different activities over time in some particular course.

14


Table 3.1: Tables created in MySQL for various events

Table Name ColumnsCourse courseId, lmsName, orgName, courseName, courseTitle, au-

thorUserId, textbackslash currConcepts, prevConcepts, course-Lang, minPrice, suggestedPrice, countryCode, endDate, start-Date

CourseForums forumId, lmsName, orgName, courseName, courseRun, com-mentSysId, commentType, anonymousMode, lmsAuthorId,lmsAuthorName, createDateTime, lastModDateTime, upVote-Count, totVoteCount, commentCount, threadType, title, com-mentableSysId, endorsed, closed, visible

CourseProblems problemId, lmsName, orgName, courseName, chapterSys-Name, sessionSysName, quizSysName, quizTitle, quizType,quizWeight, noOfAttemptsAllowed, quizMaxMarks, hintAvail-able, correctChoice

CourseVideos videoId, lmsName, orgName, courseName, chapterSysName,videoSysName, videoUTubeId, videoDownload, videoTrack-DownLoad, videoTitle, videoUTubeId075, videoUTubeId125,videoUTubeId15, videolength

CourseWiki wikiId, lmsName, orgName, courseName, wikiSlug, lmsWikiId,createdDate, lastModDate, lastRevId, ownerId, groupId,groupRead, groupWrite, otherRead, otherWrite

EventCourseInteract eventId, lmsName, orgName, courseName, courseRun, lm-sUserId, eventName, eventNo, moduleType, moduleSysName,moduleTitle, chapterSysName, chapterTitle, createDateTime,modDateTime, oldPosition, curPosition, source

EventForumInterect eventId, lmsName, orgName, courseName, eventName, com-mentThreadId, lmsUserId, queryText, noOfResults

EventProbInteract eventId, lmsName, orgName, courseName, lmsUserId, event-Name, eventNo, quizzSysName, quizzTitle, chapterSysName,chapterTitle, hintAvailable, hintMode, inputType, response-Type, variantId, oldScore, newScore, maxGrade, attempts,maxAttempts, choice, success, source, probSubTime, done,createDateTime, lastModDateTime, courseRun

EventVideoInteract eventId, sessionSysName, lmsName, orgName, courseName,courseRun, lmsUserId, eventName, eventNo, videoSysName,videoTitle, chapterSysName, chapterTitle, oldSeekTime,currSeekTime, videoNavigType, oldSpeed, currSpeed, source,createDateTime, lastModDateTime

15


Figure 3.3: Typical analytic report (Source: Open edX Insights)

16

Chapter 4

Obserations

4.1 Some Observations

Large data from tracking logs has been processed. Tracking logs generated for IITBom-

bayX are used for purpose of analysis. Records for month of June to September were

used. Summary of this is shown below.

• Summary of processed logs: Around 20 million IITBombayX tracking logs were

processed. There were JSON objects which were not in proper format are recorded.

Also, there were some events which are not documented. These logs are classified

as of now based on behavior and property of these logs. They are also recorded for

further analysis and will be reported.

• Data in MySQL: The backup of MySQL database is taken in sql dump and given

to various IITBombayX teams for experimentation.

Preprocessing of tracking also logs reveals some interesting facts about logs. Few

observations made from log preprocessing are:

• Few logs are not properly structured. They are invalidated when parsed by JSON

parser. This is because of some minor bug in some modules of code from where these

logs are generated. As we have all the information required to find exact location of

this error, we can use this to fix this kind of coding issues. On an average, we have

around 2 such cases out of 2500 logs. One such log is shown below.

17

Chapter 4. Obserations 18

Figure 4.1: Invalidated JSON objects

• There are some logs for which event are not defined. There are events name and their

fields in documentation. But these event/fields names are nowhere found. These

results can also be used to understand the issues in code that why such events are

generated and we can modify the code accordingly or document the newly found

events. Few events of such kind are shown below. For the purpose of understanding

and storing these in tables, these are classified in existing events based on their

nature.

– These event are of type navigational:

goto position, dashboard, jsi18n, i18n.js, jump to discussion, progress, view courses,

logout, how it works, calculate, jump to vertical, etc.

– These event are of type video interaction:

save user state, transcript translation, transcript download, /transcript/trans-

lation, /transcript/download, etc.

– These event are of type discussion forum:

users, reply, upvote, flagAbuse, follow, unfollow, upload, etc.

18

Chapter 5

Future Work and Conclusion

5.1 Stage-2 Work

• One pending task in tracking log cleaning is to complete the preprocessing of discus-

sion forum event, because discussion forum can give many more interesting results.

Generally, student’s interaction with the discussion forum shows that they are tak-

ing huge interest in learning. If they answers questions on discussion forum for some

topic correctly, then it shows that they have good command over that particular

topic. So, preprocessing of tracking logs is completed. Now using this and other

data modules, we can do better analysis for student performance. Then, based on

this analysis reports will be generated for teachers. These reports will then be used

to make the blended learning more effective.

• Before proceeding with any analysis, it is essential that characteristics and measures

used for this analysis are studied. As discussed in section 1.3, these measures will

be identified. Some points that will be considered in stage-2 are:

– How can we use students timeline to learn about their learning style.

– Based on their interaction with study material and quizzes, and marks ob-

tained, we can identify understanding of student for some particular module.

– What kind of reports need to be generated for teachers?

– Can we give some suggestions to students for improving their learning?

19

Chapter 5. Future Work and Conclusion 20

5.2 Conclusion

It has been shown that preprocessing of tracking logs data is necessary to get better

performance analysis results. If we don’t do the preprocessing then it is not possible to

process the logs on the go. Size of the logs are quite high and to make use of them it is

essential that they are preprocessed. Tracking log data with other data models like stu-

dents’ normal data and discussion forum data can give clear idea about students’ learning.

It is also noted that these performance reports can really help students in their self learn-

ing. Constant feedback on students’ process will also help teacher to learn more about

students learning and their performance to provide better learning experience to them.

20

References

[1] Open edX Architecture https://open.edx.org/contributing-to-edx/architecture

[2] Events in the Tracking Logs http://edx.readthedocs.org/projects/devdata/en/latest/internal_data_formats/tracking_logs.html

[3] Sample image for data analytics reporthttps://open.edx.org/sites/default/files/wysiwyg/Engagement_Chart.png

[4] EdX Insightshttp://edx-insights.readthedocs.org/en/latest/front_matter/index.html

[5] Edx Analytics Dashboardhttps://github.com/edx/edx-analytics-dashboard

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