A Personalized Learning Recommendation System Architecture for

Learning Management System

Thoufeeq Ahmed Syed

, Vasile Palade

, Rahat Iqbal

and Smitha Sunil Kumaran Nair

Middle East College, Muscat, Omam

Coventry University, Coventry, U.K.

Keywords: Technology Enhanced Learning, Learning Management System, Personal Learning Recommendation

Systems, Dataset, Educational Repositories.

Abstract: The information on the web is ever increasing and it is becoming difficult for students to find appropriate

information or relevant learning material to satisfy their needs. Technology Enhanced Learning (TEL) is an

area which covers all technologies that improve students learning. Effective Personal Learning

Recommendation Systems (PLRS) will not only reduce this burden of information overload by

recommending the relevant learning material to the students of their interest, but also provide them with

“right" information at the “right" time and in the “right" way. In this paper, we first present a detailed

analysis of existing TEL recommendation systems and identify the challenges that exist for developing and

evaluating the datasets. Then, we propose an architecture for developing a PLRS that aims to support

students via a Learning Management System (LMS) to find relevant material in order to enhance student

learning experience. Also we proposes a methodology for building our own collaborative dataset via

learning management systems (LMS) and educational repositories. This dataset will enhance student

learning by recommending learning materials from the former student’s competence qualifications. The

proposed dataset offer information on the usage of more than 19,296 resources from 628 courses apart from

data from social learner networks (forums, blogs, wikis and chats), which constitutes another 3,600 stored

files Finally, we also present some future challenges and a roadmap for developing TEL PLRSs.

1 INTRODUCTION

Technology Enhanced learning is the application of

information and communication technologies to

teaching and learning (Kirkwood and Price 2014).

Recommendation Systems (RS) are software tools

based on machine learning and information retrieval

techniques (Aamir and Bhusry, 2015) that provide

suggestions for potential useful items to someone’s

interest (Ricci et al., 2011). RSs’ are widely used in

many fields including TEL (Verbert et al., 2012).

Until recent years Learning Management Systems

(LMS), a subset of TEL, had not been personalized.

Several researchers working in the field of LMS to

enhance students learning experiences highlighted

the need of RSs’ for LMS, so as to address the

following challenges in LMSs’:

 Difficulty in sharing the learning resources;

 High redundancy of learning material (Shishehchi

et al., 2011);

 Personalization of information (Fischer, 2011);

 Information overload (Manouselis et al., 2009)–

which is the ever increasing volume of digital

information particularly on the web, and due to

this reason it has becomes extremely more and

more difficult for learners to find suitable items to

satisfy a particular need;

 Learning isolation.

Traditional LMSs only describe the basic

information learning resources but could not describe

the complex relationship between resources,

teachers, students and their peers (Poorni et al.,

2014). Also such systems could not integrate

learning from formal, informal and social network

learners (Fazeli et al., 2014).

This paper introduces a novel architecture for RS

via LMS, Moodle in our case. Learning-related

search problems are highly recurrent across a group

of students participating in similar teaching activities.

The process of discovering the optimal search query

Syed T., Palade V., Iqbal R. and Nair S.

A Personalized Learning Recommendation System Architecture for Learning Management System.

DOI: 10.5220/0006513202750282

In Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (KDIR 2017), pages 275-282

ISBN: 978-989-758-271-4

in a search engine like Google is repeated by all

students participating in a particular module and the

knowledge of the most optimal search query is not

captured or shared across the community of students

even though sharing this knowledge would

significantly increase the learners’ performance

(Zaina et al., 2010). Recommendation systems in

education filed should be personalized by the

objectives of the task rather than on the preferences

of students (Losada and Martín, 2014). Online social

networks allow users to share ideas, activities,

events, and interests within their contact network

(Prates et al., 2013). This research project will prove

the feasibility of using recommendation systems and

social media applications in educational

environments to deliver a working prototype of a

recommender system that will assist the learner, and

this implies learning from or reusing previous

students querying experience, activity-based context

which is based on users actions (Kramár and

Bieliková, 2012), use of social media interactions

among students. In section 2 we presents state-of-the

art and current RSs’ architectures and introduces

personalized learning RS for TEL. In Section 3 we

present the proposed PRS architecture. Section 4

presents the Datasets for TEL RSs’ and associated

challenges. Section 5 presents the challenges in

evaluating the RSs’ for TEL. Finally section 6

presents conclusions and direction for future work.

2 STATE OF THE ART AND

CURRENT RS

ARCHITECTURES FOR TEL

2.1 Recommendation Systems

There has been massive growth in research on RSs’

and the applications of RSs to various areas in the

last 15 years (Candillier et al., 2009). The following

are the commonly proposed approaches for building

RSs’:

2.1.1 Content based Filtering

Content based RSs’ predict an item to a user based

on the similarity between the items content

description and the user’s preferences model

(Pazzani and Billsus, 2007). Content based RSs’

work with user and item profiles which were referred

in the past. The referred profiles are represented as

vectors, which hold the characterizing attributes of

items or users. (del Losada and Martín, 2014) has

developed a Prototype of Content-Based RS in an

Educational Social Network. The experiments were

carried on in a real time environment ClipIt (elgg,

2016) tool. However, the recommendations

generated are uniform for all the students and lacks

personalization

. To enhance students learning experience

(Hsu, 2008) has developed an online personalized

English learning RS which facilitates English as a

Second Language (ESL) student with reading lessons

that suit individual student preferences.

2.1.2 Collaborative Filtering

Collaborative filtering techniques are most

commonly used in social networking and social

media environments for proposing user interactions

or interesting shared resources (Karampiperis et al.,

2014). Collaborative RSs’ aims to predicts individual

preferences and provide suggestions for links for

further resources or other systems, products and

resources which are likely to be of interest. (Dascalu

et al., 2015) developed an educational collaborative

filtering recommender agent (U Learn) with a build-

in integrated learning style recommender. Such RS

helps in providing suggestions and shortcuts for

learning materials and learning tools, helping the

learner to better navigate through educational

resources. The limitation of such RS are

recommendations made by the learners with similar

learning styles.

2.1.3 Knowledge based Filtering

Knowledge-based RSs integrate the user knowledge,

items or products in order to provide

recommendations. This filtering technique mark the

items for recommendations based on historical data

information, which is followed by the development

of an information recommender by using logical

reasoning technology (Burke, 2002). (Manouselis et

al., 2011; Zaina et al., 2011 and Zapata et al., 2013)

highlighted the need of RSs for TEL based on a

literature review which focuses on the availability

and ever increasing quantity of digital learning

resource repositories and from the outcomes of

Social Information Retrieval for Technology

Enhanced Learning (SIRTEL) annual workshop

series and a Special Issue on Social Information

Retrieval for TEL and proposed a DELPHOS and e-

LORS (e-learning object recommender system)

which are integral and intelligent solution for the

recommendation of learning objects (LO) stored in a

repository in which the recommendation are provided

in an ordered list of LOs’.

2.1.4 Hybrid Filtering

Hybrid RSs involve are the combination of

collaborative filtering and content-based filtering

techniques. (Adomavicius and Tuzhilin, 2005; Gu,

2013; Manouselis et al., 2012; Ricci et al., 2011)

(Ricci et al., 2011) suggest four different types of

hybrid recommenders: Separate collaborative and

content-based RSs; Collaborative RSs with added

features of content-based filtering method; Content-

based RSs with added features of collaborative

filtering method. (Poorni et al., 2014) presents a

personalized e-learning hybrid RS using the concept

of “Fuzzy Tree Matching” by considering the key

factors such as 1) learning activities and learners’

profiles 2) learning activities and 3) pedagogical

issues. (He et al., 2014) proposed A Social

Recommender System called as SRSH based on

Hadoop parallel computing platform. SRSH system

integrates content-based and collaborative filtering

techniques to further improve the performance of

recommendation. (Khribi et al., 2012) presents a

framework

for building automatic recommendations

in e-learning platforms which consists of two

modules: an off-line module and an online module.

The first module pre-processes the data to build user

and content models. Online module uses these

models dynamically to find the user’s requirement

and goals, and predicts a list of recommendation.

User preferences objects are obtained by using a

“range of recommendation strategies” which are

primarily based on hybrid (content-based filtering

and collaborative filtering) filtering approach.

2.2 Personalized Learning RSs

Why personal? Every user, students in our case, has

individual needs and particular requirements. Some

of the students are highly self-motivated and learn by

exploring while other students prefer some specific

guidance in a structured way. With the ever growing

computer and internet technologies available, many

universities use LMSs’ to support teaching and

learning. Presently many LMSs’ are available as

proprietary solutions and open source (Santos and

Boticario, 2011). Despite the diversity in term of core

functionalities, these LMSs’ share a few

commonalities such as calendar, files, storage,

forums, wikis, blogs, etc. Education learning

systems, in particular LMS can further improve

teaching and learning practices of learners if

supported by personalized recommendations (Hauger

and Köck, 2007) and accessibility issues (Moreno et

al., 2009). The research finding in (Dagger et al.,

2007) shows that future LMSs’ primarily focus on

service-oriented architectures to incorporate social

media aspects into LMSs’.

3 A PROPOSED LMS PRS

ARCHITECTURE

In this section we present the proposed architecture

for a LMS recommendation system for

recommending learning material to students, which is

the primary objective of this paper. We selected

Moodle LMS in this case. The proposed architecture

is given in “Figure:1” and it consists of three main

components ‘learning material data source’, ‘seeking

student information’, and ‘generation’.

‘Learning material data source’ component is the

primary data source and this component is the

building block for designing and building our own

dataset for this research project. The dataset

developed here constitutes the knowledge base of

both formal and informal learners. The output of this

component is the preprocessed data which is

collected from:

 MEC(Middle East College) Moodle Database -

former students competence qualifications;

 Social Learners Network - wikis, forums, blogs

@ MEC LMS;

 Other institutes LMS Servers. i.e., other three

institutes in Oman.

The recommendation from all the above can take

hybrid filtering technique though well-defined

educational metadata sources and educationally

influenced filtering decisions. The methodology

given in (Karampiperis and Diplaros, 2007) will be

applied, which generate a matrix to represent the

educational attributes of the learning resources. The

recommendation from all the above can take hybrid

filtering technique though well-defined educational

metadata sources and educationally influenced

filtering decisions. Additional filtering process can

be applied on this matrix based on an educational

“footprint” followed by the learners of these three

data sources.

The data acquired from MEC Moodle and LMS

of other institutes, connected using state of the art

“mutual authentication technology” which uses

Transport Layer Security protocol (Sheffer et at.,

2015), consist of formal learning data as well as

informal learning data acquired through social

learners’ networks.

Pre-processing of data allows the original data

from the above three sources to be transformed into a

Figure 1: Proposed architecture of a Personalized Recommendation System.

suitable form to be used by a data mining association

algorithm. The data pre-processing tasks in this

component includes data filtering, year and semester

wise session identification, student profiling, path

completion, transaction identification, data

transformation and enrichment, data integration, data

reduction to generate an explicit learning experience.

‘Seeking student information’ component

represents the various characteristics of the student,

including students profile creation, that can be used

to generate an implicit learning experience

using the

data mining association rules. This involves

understanding the “learning material attributes”,

which can be obtained from student’s current Moodle

session or previous logs of the student in social

learner network. Building student requirements

cannot be justified only by precise values, but also a

multi-criteria analysis approach need to be applied.

This is done by developing a multi criteria decision

model using linear weighted sum or a more rigorous

approach based on fuzzy set technology. The student

preferences and learning behavior can be defined

using the following three steps:

1. Clicks: This action defines the shortlisting of

material.

2. Selection: This action is defined as material

selected and added to cart.

3. Learning: This action is using or reading the

material.

The above three behaviors are used to identifying

the learners relative preferences LP

for each of the

material referred from the data sources

. The formula

used is:





 











ijMj

LPMinLPMax

LPMinLP

||1||1

||1







 









ijMj

LPMinLPMax

LPMinLP

||1||1

||1







 

ijMj

LPMinLPMax

LPMinLP

||1||1

||1







and

denotes the number of references

to material through clicks, selection and learning

actions made by the learner i for material j

respectively.





ijMj

LPMax

||1 





ijMj

LPMax

||1 

and





ijMj

LPMax

||1 

denotes the maximum

number of clicks, selection and learnings for a

learner i for M material.





ijMj

LPMin

||1 





ijMj

LPMin

||1 

and





ijMj

LPMin

||1 

denotes the

minimum number of clicks, selection and learnings

for a learner i for M material.

The first stage of the ‘Generation’ component is

to apply data mining rules and techniques on the

outputs of ‘learning material data source’ and

‘seeking student information’ components and

forward the results to a Hybrid RS. The objective of

this is to model students’ information seeking

behavior in order to develop a personalized

information retrieval system to enhance students’

learning experiences and such a PRS must have the

features given below. Students’ profile database has

the logs and records of learning styles, learning

material access and other profile of student with

respect to his/her specialization. These records and

logs will be used to identify the student’s preferences

with peers.

Some feature of educational RSs are suggested in

(López et al., 2015) and includes: monitoring

behavior, heuristics to infer information, user

feedback, filtering rules, navigation history, internal

database of items, similarity matching. The proposed

PRS will have the following features:

 Good user interface design;

 Real time recommendation as a service;

 Accuracy of identifying student requirements;

 Structured and accurate finding of recommended

learning materials;

 Implicit and explicit recommendation of learning

material.

4 DATASETS FOR TEL RECSYS

In the last decade many researches have development

RSs for TEL but only a few of them validated these

RSs based on real time scenarios (Dascalu et al.,

2015). (Drachsler et al., 2010; Manouselis et al.,

2010) raises the issue of missing data sets for

recommender systems in TEL that can be used as

benchmarks to compare different recommendation

approaches. The availability of datasets helps in

drawing stronger conclusions about the validity and

generalizability of scientific experiments. This also

helps researchers to compare the experimental results

based on large datasets that capture learner

interactions in real settings. Furthermore, educational

datasets can support research advances on TEL

towards a basic theory for TEL (Verbert et al., 2011)

by offering the recorded and observed behaviour of

the stakeholders (students, teachers, parents, lifelong

learners, educational institutes) in different formal

and informal learning settings. (Verbert et al., 2011)

suggested guidelines for assembling suitable

datasets. We proposed a framework for building our

own dataset, “Figure: 2”, for the RS architecture

given in “Figure: 1”. The building of proposed

dataset framework is based on the guidelines given in

(Verbert et al., 2011) which include:

a. Create a data set that realistically reflects the

variables of the learning setting”;

b. “Use a sufficiently large set of user profiles”;

c. “Create data sets that are comparable to others”.

5 EVALUATING TEL RSS

An evaluation of an interactive system ensures that it

behaves as expected by the designer and that it meets

the requirements of the user (Dix, 2009). (Ricci et al.,

2011) has provided some evaluation requirements of

TEL RSs and has proposed a four layer general

guidelines framework to evaluate the success of TEL

RSs: 1. Reaction of user, 2. Learning, 3. Behaviour,

4. Results. (Thai-Nghe et al., 2010) presented a

protocol for used for TEL RSs evaluation. The

evaluation algorithms were implemented in

MyMedia open source framework and the results

were compared with traditional methods such as

logistic regression or linear regression. (Drachsler et

al., 2009) highlighted the limitations that exists in the

evaluation of TEL RS due to unavailability of

datasets. They also suggested the framework given in

Table 1 for the analysis of TEL RSs.

Table 1: (adapted from (Drachsler et al., 2009)): An

evaluation framework for RSs in TEL.

Measurements Parameters

Technical

measures

1. Accuracy

2. Performance

Educational measures

1. Effectiveness

2. Efficiency

3. Satisfaction

Social network measures

1. Variety

2. Centrality

6 CONCLUSIONS AND FUTURE

WORK

In this paper we have presented a study of

evaluations of recommendation systems for TEL by

raising several concerns, issues and challenges

encountered by these systems. We also proposed a

personalized learning material recommendation

system architecture and discuss related technologies.

The proposed architecture has good

characteristics in recommending students to choose

appropriate learning materials for their assessments

Figure 2: Architecture building dataset for personalized material recommendation system.

by providing relevant recommendations. In the next

phase of this research project the framework will be

implemented as a plugin for the MEC Moodle to be

tested and validated, but not limited to, on the newly

developed dataset.

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