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Vol. 6, No. 3, pp. 228-244

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Mining Students’ Data to Analyse Usage Patterns in
eLearning Systems of Secondary Schools in Tanzania

Joel S. Mtebe and Aron W. Kondoro

The University of Dar es Salaam, Tanzania

Abstract: The adoption and use of various eLearning systems to enhance the quality of education in
secondary schools in Tanzania is becoming common. However, there is little evidence to suggest
that students actually use them. Existing studies tend to focus on investigating students’ attitude
towards using these systems through surveys. Nonetheless, data from surveys is normally subject
to the possibility of distortion, low reliability, and rarely indicate the causal effects. This study
adopted WEKA and KEEL as data mining tools to analyze students’ usage patterns and trends
using 68,827 individual records from the log file of the Halostudy system implemented in
secondary schools in Tanzania. The study found that the system usage is moderate and in decline.
There is also variability in the usage of multimedia elements with biology having the highest
number while mathematics has the lowest. Students from Dar es Salaam, Mwanza, and Arusha, in
that order, had the highest system usage with the lowest being from the peripheral regions. The
possible challenges limiting system usage are discussed. These findings show that data mining tools
can be used to indicate usage patterns of systems implemented in sub-Saharan Africa and to help
educators to find ways of maximising systems usage.

Keywords: educational data mining, learning analytics, eLearning systems, eLearning secondary
schools.

Introduction
The adoption and use of various eLearning systems, such as Moodle, Sakai, and Blackboard, to
improve the quality of teaching and learning at various levels of education in sub-Saharan Africa is
becoming common (Ssekakubo, Suleman, & Marsden, 2011; Unwin et al., 2010; Venter, Rensburg, &
Davis, 2012). In Tanzania, for instance, more than 50% of surveyed institutions were found to have
installed eLearning systems of various kinds (Mtebe & Raisamo, 2014). In the beginning, the majority
of these systems were implemented within higher learning institutions. In recent years, many of these
systems have increasingly been implemented in secondary schools. Some good examples of eLearning
systems implemented in secondary schools in Tanzania include the Tans-eL system (Kalinga, Bagile,
& Trojer, 2006), Retooling system (Mtebe, Kibga, Mwambela, & Kissaka, 2015), Christian Social
Services Commission system (CSSC, 2014), Shule direct system (Mtebe & Kissaka, 2015), and Brain
share system (Mwakisole, Kissaka, & Mtebe, 2018).

With the increased number of students, which stretches beyond the limit of available teachers and
learning resources, these initiatives aim to provide digital content accessible via the Internet where
students can use these resources to enhance their learning activities. The majority of these initiatives
aim to provide quality digital content to students with minimum intervention from teachers. For
instance, the Shuledirect system has digital content for various subjects such as Physics, Chemistry,

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Biology, English, Geography, Civics, Mathematics and Kiswahili (ShuleDirect, 2019). The Tans-eL
system has digital content for Mathematics, Biology, and Chemistry (Kalinga et al., 2006).

Despite the continued investment in these systems, there is little evidence to suggest students across
the country actually use them. These systems cannot help students improve learning if they are not
used. Many studies have strongly shown that there is a correlation between eLearning system usage
and students’ performance (Filippidi, Tselios, & Komis, 2010; Jo, Kim, & Yoon, 2014) and students’
satisfaction (Naveh, Tubin, & Pliskin, 2012; Tarigan, 2011). Despite these benefits, the lack of actual
usage or underuse of systems implemented in sub-Saharan Africa is a common problem (Ssekakubo et
al., 2011; Unwin et al., 2010), and has been a major setback against their success (Bervell & Umar, 2018;
Lwoga, 2014). The need to ensure that students make full use of these systems is important so that the
anticipated benefits are attained.

Many of the existing studies have focused on students’ attitude towards using these systems through
surveys (Mselle & Kondo, 2013; Msoka, Mtebe, Kissaka, & Kalinga, 2015). The findings from the
majority of these studies tend to indicate students have positive attitudes but when it comes to actual
usage their attitudes are more reserved. Moreover, data from surveys is normally subject to the
possibility of distortion and low reliability and rarely indicate the causal effects (Jo et al., 2014).
Therefore, there is a need for more sophisticated means for investing how students use these systems,
which is important to avoid inefficient investments and ensure maximum utilisation of the installed
systems.

Recently, data mining technologies have been making a lot of headway in capturing and analysing
massive amounts of data generated by these systems (Romero, Ventura, & García, 2008). The
eLearning system keeps a record of all the activities that students perform in the log files which can be
analyzed and used to provide immediate feedback to educators (Romero, Espejo, Zafra, Romero, &
Ventura, 2013). Despite these great potential benefits of data mining technologies, few studies have
utilised them in investigating eLearning system usage amongst students in secondary schools in sub-
Saharan Africa and Tanzania, in particular.

This study utilised data from a Halostudy system (https://halostudy.ac.tz/) log to investigate students’
usage patterns in the system implemented in secondary schools in Tanzania. The Halostudy system
was customised from the Moodle system to suit the context of secondary education. The study
adopted WEKA and KEEL as data mining tools, involving 68,827 individual records, accessed the
system for nearly 14 months. The findings from this study show that data mining tools can be used to
indicate usage patterns of systems implemented in sub-Saharan Africa and help educators to find
ways of maximising systems usage. The description of the Halostudy system is explained next.

The Halostudy System
The implementation of the Halostudy system can be traced back to 2013 when the Ministry of
Education and Vocational Training (MoEVT) of the government of Tanzania implemented the
retooling project in collaboration with the College of Information and Communication Technologies
(Mtebe, Kibga, et al., 2015). The retooling project in this context was a project aimed at reskilling or
upgrading subject content knowledge of teachers in science and mathematics subjects in secondary
schools in Tanzania. Generally, the project aimed at addressing the low success rates of students in
science and mathematics subjects in secondary schools through enhancing teachers’ subject content

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knowledge of the subjects they teach. It was claimed that the failure rates of students in these subjects
were linked to inadequate secondary school teachers’ knowledge on the subjects.

To address this problem, under the retooling project, the multimedia enhanced digital content (in the
form of animations, simulations, video, and audio) were developed and shared with teachers across
the country via the customised Moodle system. The developed content was for only topics perceived
to be difficult to understand for an assessment conducted in various regions in Tanzania. A total of 70
topics and 147 subtopics were developed and extensively supported with multimedia elements. More
specifically, 93 videos were recorded, 57 animations were developed, and 201 still pictures were
captured and integrated into the digital content (Mtebe, Kibga, et al., 2015). The developed
multimedia enhanced content was then pilot tested with 2,000 teachers in 858 schools located in 13
regions in Tanzania. During the pilot phase, the developed multimedia enhanced content was
uploaded into the customised Moodle system where teachers accessed the system for three months
before conducting a follow-up study using SMS quizzes. The result showed that multimedia enhanced
content helped to improve subject content knowledge of teachers who participated in the retooling
project (Mtebe, Kondoro, Kissaka, & Kibga, 2015). In a separate study, the customised Moodle system
was perceived to be easy to use by the majority of surveyed teachers (Mtebe, Mbwilo, & Kissaka,
2016). More details about the retooling project can be obtained in Mtebe, Kibga, et al., 2015.

Building from the success of the retooling project, the College of Information and Communication
Technologies (CoICT) in partnership with Viettel Tanzania Ltd, aka Halotel, developed an Internet
based application with multimedia enhanced content drawn from the retooling project for all topics of
science and mathematics subjects for secondary education in Tanzania. While the retooling project
focused on enhancing teachers’ subject content knowledge of the subjects they teach, this project
focused on students. This is because the content developed during the retooling project could be used
by students to enhance their subject content knowledge as this was the same content used by the
teachers.

In order to disseminate the content and ensure that many students benefit from it, the College entered
into an agreement with Halotel to use its network to facilitate dissemination of the content to the
learners in secondary schools in Tanzania. The Halotel has connected 427 secondary schools with the
Internet, which could potentially benefit from the developed content. The content was made free of
charge to the students who had access to the Internet through www.halostudy.ac.tz. Moreover, the
Halostudy app was also developed for those who have mobile devices and is available at the
GooglePlay Store. Figure 1 shows an interface of the Halostudy platform.

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Figure 1: The interface of the Halostudy system. (Source: https://halostudy.ac.tz/)

Since the launching of Halostudy in August 2017, students all over the country have been accessing
the content to enhance the content knowledge of their subjects. Just like other similar initiatives, the
Halostudy system was meant to facilitate students’ centered learning; whereby students could access
and use the multimedia enhanced content anywhere, anytime, without interaction with teachers.
Similarly, teachers could also use the content to enhance their subject content knowledge for
complementing classroom environments. Despite the continuous use of the Halostudy system
throughout secondary schools in Tanzania, little information on how students use the system and
what features are mostly used was available. In this study, we utilised data from a system log to
investigate students’ usage patterns in the Halostudy system using WEKA and KEEL as data mining
tools.

Related Works
The eLearning systems tend to keep records of all the activities that students have performed in the
form of a log file. The activities which are kept in the log file include the time, Internet Protocol
address, name of the student, the action completed, and the activities performed in different modules
(Kadoic & Oreski, 2018). Due to the large quantities of data these systems can generate daily, it is very

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difficult for educators to manage them manually (Estacio & Raga, 2017; Romero et al., 2008).The data
mining tools have adapted and been used to explore the unique types of data from eLearning systems
to better understand how students learn and identify the settings in which they learn to improve
educational outcomes (Romero & Ventura, 2013).

Given these advantages, studies have been using these mining tools to investigate systems features
that have an influence on improving students’ learning performance. For instance, Macfadyen and
Dawson (2010) used data mining tools to analyse students data from a Blackboard system. Authors
extracted the total number of discussion messages posted, the total number of mail messages sent, and
the total number of assessments completed as key variables. Through regression analysis with the
final grade, it was found that the variables explained more than 30% of the variation in student final
grade.

Similarly, Mwalumbwe and Mtebe (2017) extracted data from the Moodle log of two courses delivered
at the Mbeya University of Science and Technology, using a developed mining tool and subjected into
linear regression analysis with students’ final results. The study found that discussion posts, peer
interaction, and exercises were determined to be significant factors for students’ academic
achievement in blended learning. However, time spent in the system, number of downloads, and
login frequency were found to have no significant impact on students’ learning performance.

Kadoic and Oreski (2018) used Moodle plugins to extract data from the log file and analyse students’
behavior at the Faculty of Organization and Informatics at the University of Zagreb. The results of the
students’ behavior were interpreted in a bid to improve students’ learning. Similarly, Romero et al
(2013) extracted data from Moodle logs and used them to predict student performance. Generally, the
study found that the regular usage of features such as assignments, quizzes, and forum activity had
an impact on students’ final grade.

In general, these studies and many others, such as those in Estacio and Raga (2017); Hung and Zhang
(2008); Kotsiantis, Tselios, Filippidi, and Komis (2013); Lotsari, Verykios, and Panagiotakopoulos
(2014); Podgorelec and Kuhar (2011); Wen and Rose (2014); Yu and Jo (2014); and Zorrilla, García, and
Álvarez (2010), have successfully used data mining tools to predict students’ performance based on
log data from learning environments. Very few studies applied data mining tools to analyse log data
to provide informed feedback on how students use eLearning systems in secondary schools in
Tanzania. In a review of 74 articles, published between 2007 and 2017, Mtebe and Raphael (2018)
found that the majority of articles focused on users’ attitude and perceptions towards using these
systems. With increased adoption and use of these systems in secondary schools in Tanzania, the use
of data mining tools that will use log data to inform educators on the usage pattern is increasingly
important.

Methodology
Research Design

This study is focused on investigating the access pattern of students using the eLearning system. We
were interested in understanding how the system was being used and by whom. To find this out, we
started by investigating the level of access based on the identified variables as shown in Table 1.

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Table 1: Variables Extracted for the Study
No. Variable Description
1. Course Name Identification string of the course in which the action is related. This variable

helped to differentiate between various courses accessed in the system

2. Source of Access This data enabled to identify the region/places where users accessed the system

3. Type of Content The system has text, video, animations, and simulations as types of content. This
variable aimed to find out to what extent users have been using various types of
the content installed in the system.

4. Time Date and time stamp of when the action was executed which enabled to estimate
the geographical location of users when they accessed the system.

5. IP Address Unique numerical label assigned to the device used by the user in order to
determine the type of the device used when accessing the system.

6. Action Type of action initiated which enabled to determine the number of most and least
active users used the system. This number was later grouped per months.

Using variables in Table 1, we were able to get a general picture on the behavioral pattern of students
who used the system. To do so, usage data was extracted from the Halostudy system implemented in
secondary schools in Tanzania in the form of text-based logs. These logs are a record of time-based
events that occur in the system. Every time a user performs a certain action, information about it is
recorded in the logs. The recorded data contains attributes with variables shown in Table 1. The data
was extracted from the time the system was launched in August 2017 to the time that this study was
conducted (September 2018) and exported to a CSV file. The extracted file had a total of 68,827
individual records of raw data before being further analysed using data mining tools. The data mining
tools used to analyse the obtained data are explained next.

Data Mining Tools and Data Analysis

The Halostudy is based on the Moodle platform and therefore the data mining tools compatible with
Moodle system were selected and adopted. Therefore, Waikato Environment for Knowledge Analysis
(WEKA) and Knowledge Extraction based on Evolutionary Learning (KEEL) data mining tools were
adopted in extracting data from the Halostudy system in the form of text-based logs. The WEKA
(https://www.cs.waikato.ac.nz/~ml/weka/index.html) is a an open source software, written in Java,
aiming at allowing users to compare different machine learning methods on new data sets (Hall et al.,
2009). It consists of visualisation tools and algorithms for data analysis and predictive modeling,
together with a graphical user interface for easy access to these functions (Holmes, Donkin, & Witten,
1994).

The KEEL tool (http://www.keel.es/) is also an open source software that supports data management
and the design of experiments. The KEEL pays attention to the implementation of evolutionary
learning and soft computing based techniques for data mining problems including regression,
classification, clustering, pattern mining and so on (Ernández, Uengo, & Errac, 2011). The WEKA and
KEEL tools were selected due to the fact that they are both open source tools, developed in Java, and
use the same dataset external representation format (ARFF files). The CSV file was exported into the
WEKA in order to generate some patterns based on the identified variables in Table 1. In areas where

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the WEKA tool had limitations in generating the intended variables, the KEEL tool was used. The
data from WEKA and KEEL tools were exported to Microsoft Excel for analysis with variables being
grouped in the form of tables, charts and graphs for easy understanding.

Ethical Issues

This study used data mining tools to extract data from the Halostudy system in order to understand
usage patterns amongst users from the onset of the project. However, like many other data mining
studies, the issues of security, privacy, and individuality of data need to be respected and protected to
make sure that people are judged and treated fairly (van Wel & Royakkers, 2004). In this study, the
extracted data were treated confidentially by ensuring that the identified variables that might identify
users were excluded. The excluded information included login credentials, personal user profiles, and
personal details from quizzes, and the user’s data in discussion activities.

Findings
Access per Subject

The level of user activity for each subject was determined by comparing the access percentage of each
subject. Therefore, the total number of records for each subject was expressed as the percentage ratio
of the total number of records in the whole file. Of the 68,827 individual records, there was a small
difference in access levels across the four subjects with biology having the highest number of accesses
(19,960) equivalent to 29% compared to other subjects. Chemistry and physics had the lowest with
23% each as shown in Figure 2.

Figure 2: The percentage of users’ access per subject.

In Tanzania’s education system, students are allowed to drop chemistry and physics subjects at Form
II if they are going into arts streams, while biology and mathematics are normally compulsory
subjects. Therefore, those who accessed biology are likely the same students who accessed
mathematics. On the other hand, the low percentage in chemistry and physics could be due to the fact
that some students who dropped these subjects at Form II did not access them in subsequent classes,
making a slightly small difference in access percentages.

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Biology Chemistry Maths Physics

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Number of Users per Range of Access

The activity patterns in terms of numbers of most and least active users were analysed. Users were
categorised into different groups depending on the number of times they accessed the system while
filtering records with duplicate users. Each group had an access range of 100, starting from 0-100,
100-200, and so on. The study shows that the majority of users accessed the system between 0-100
times followed by 102 to 201 times. Few users accessed the system 1400 times plus in the studied
period. Figure 3 shows the number of users per range of access for the studied period.

Figure 3: The number of users per range of access in the Halostudy system.

The study has shown that the access levels of students is moderate, with the majority of students
accessing the system in a range starting from 0-100 in nearly 14 months. Clearly, despite 68,827
individual records available in the system, the majority of students do not access the Halostudy
regularly. There are many reasons which could have contributed to this irregularity of Halostudy
access, some of which are discussed in the challenges section in this study.

Multimedia Access per Subject

The multimedia content such as audio, video, and animations play a key role in the learning process.
They are thought to enhance the understanding of abstract and difficult concepts that cannot be easily
grasped from words alone (Steinke, Huk, & Floto, 2003). Moreover, they are thought to support
students with different learning styles by presenting content in a variety of multimedia (video, audio
and sound) (Woodcock, Burns, Mount, Newman, & Gaura, 2005). As shown in Figure 4, there is
variability in the usage of multimedia elements, with biology having the highest number (more than
1500 times) while mathematics has the lowest.

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Total number of users per range of access

Total

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Figure 4: The number of users per multimedia access per subject.

Multimedia Access per Subject per Form

The access of multimedia elements was also grouped per year of study from Form I to Form IV, in
order to determine which cohort had accessed mostly the multimedia elements. The study shows that
Form II and Form III students had the highest access of multimedia elements throughout the three
subjects (see Figure 5). Interestingly, only a few Form IV students accessed multimedia elements
nearly 100 times.

Figure 5: The number of users per multimedia access per class of study.

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Access per Month

The level of user engagement can change over time depending on various factors. The access
frequency per month starting from when the system was launched were extracted and analysed. The
results show that the most users accessed the system in the month of January 2018, followed by
November 2017. However, the trend shows that the number of users accessing the system has been
decreasing towards the end of September 2018. In fact, August 2018 and September 2018 had the
lowest number of users who accessed the system as shown in Figure 6.

Figure 6: The number of users accessed the system per month.

The trend indicates that many students tend to access the system between November and December.
This finding could be due to the fact that that many students tend access the system close to the final
exams. Final year exams are normally conducted between November and early December in the
majority of secondary schools in Tanzania. Another possible explanation could be that students tend
to have access to the Internet during the holidays and outside school premises (Mwakisole et al.,
2018). The majority of students are normally in holidays in December and January each year in the
Tanzanian education system.

Web vs Mobile app Access

The system’s mobile version was also developed to provide access to those with access to mobile
phones. Therefore, we were interested to find out the media that students used the most when
accessing the system. Interestingly, 71.7% of students accessed the system using the web while 28.3%

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2017 2018

Access frequency per Month

Total

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of students accessed the system via mobile app. This result may be explained by the fact that the use
of mobile phones are strictly prohibited in many secondary schools in Tanzania (Kafyulilo, 2014;
Kihwele & Bali, 2013) despite many students having access to them (Chambo, Laizer, Nkansah-
Gyekye, & Ndume, 2013; Malero, Ismail, & Manyilizu, 2015; Mwakisole et al., 2018; Tarimo &
Kavishe, 2017). Therefore, the use of mobile phones could not make much difference in helping
students to access the system.

Access per Location

Users were also categorised on geographical location in order to visualise the accessibility and usage
of the system across the country. Therefore, the total number of records originating from each region
was calculated. Generally, an IP address value was used to estimate the geographical location while
the IPInfo (IPinfo, 2018) was used to convert IP addresses to specific regions. Figure 7 shows the
distribution of the percentage of users accessing the system per region.

Figure 7: The percentage of users accessing the system per region.

The findings show that many students who accessed the the system are from big cities with good
Internet connectivity — Dar es Salaam, Mwanza, and Arusha, in that order. The lowest accessed
regions were those located in peripheral areas such as Kagera, Mara, and Shinyanga. This finding
confirms the fact that access to reliable Internet in rural areas is still a problem. The government has
been making considerable efforts to roll out fiber optical cable, including the East African Submarine
Cable System, SEACOM, and the East African Marine System, in order to widen access to the Internet
in the rural areas (Mtebe & Raphael, 2018). It seems, therefore, these initiatives have not benefited
many users in rural areas.

Possible Challenges Limiting Access to the Halostudy System
The study has shown that the students’ access of the Halostudy system is moderate, with the majority
of students having accessed the system in a range starting from 0-100 in nearly 14 months. It was also
interesting to note that many students tend to access the system during the holiday months. Some of

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the possible challenges that could have hindered the access and use of Halostudy system are as
follows:

Lack of Computers in Schools

The government and its partners have been making considerable efforts towards equipping schools
with computers and other ICT facilities. Recently, the government equipped approximately 31.4% of
government secondary schools (out of 3,601) with computers ranging from 1 to 68 computers, with
20.1% them being connected to the Internet (MoEST, 2017). Similarly, Halotel supported 400 schools
and the Tigo firm supported 700 schools with computers connected to the Internet in selected regions
of the country (Kazoka, 2016; Tanzania TELECOMS, 2016). Despite these efforts, and many others,
many secondary schools in Tanzania do not have computers (Muhoza, Tedre, Aghaee, & Hansson,
2014) which limits students from accessing the system. It should be noted that the Halostudy is an
Internet based system and therefore schools need to have computers connected to the Internet.

The Cost of Internet Connectivity

The cost of Internet remains a major challenge to the access of eLearning systems in Tanzania. The cost
of connecting 300 computers was estimated to be 4 million TShs (2140€ ≈ 3100$) per month for a
dedicated 704kb/128kb satellite connection (Tedre, Ngumbuke, & Kemppainen, 2010). This is
definitely unaffordable to many secondary schools in Tanzania, given the fact that they depend on
government funds to run most of their services. Although use of the mobile Internet could be a
solution to the majority of schools, the cost of Internet bundles provided by many mobile firms is still
high (Mtebe & Raphael, 2018). For instance, the subscription of 10GB of Internet cost around US$ 25
per month, which is expensive to the majority of students. In a study conducted in seven schools in
Dar es Salaam, it was found that the majority of students were paying less than Tsh 1000/ = (US $ 0.5)
for the Internet per week via their mobile devices. Despite the availability of special student bundles
(1GB per week @ Tsh 1500 [0.6 USD]), many students cannot afford them (Ghasia, De Smet,
Machumu, & Musabila, 2018).

Attitudes on the Use of Mobile Phones

Studies have shown that mobile phones can compensate for a lack of existing infrastructure and
erratic Internet connections in sub-Saharan Africa and Tanzania, in particular (Chambo et al., 2013;
Ghasia et al., 2018; Joyce-Gibbons et al., 2018). However, teachers’ and parents’ negative perceptions
and attitudes towards students using mobile phones in schools continues to be a limiting factor.
Teachers and parents believe that mobile phones have a detrimental effect on student performance
and moral values (Kihwele & Bali, 2013) and that students tend to misuse them by watching
pornographic and entertainment materials instead of studying (Kafyulilo, 2014). Therefore, while it
might be possible for students to access these devices informally, they cannot bring them to school or
use them regularly at home limiting the possibilities of using them for accessing eLearning systems.

Inadequate ICT Skills

The use of Halostudy requires students to have the skills of using computers and the Internet.
Nonetheless, many students do not have adequate skills to use ICT facilities and the Internet,
especially in rural areas (Barakabitze, Kitindi, Sanga, Kibirige, & Makwinya, 2015; Tedre et al., 2010).
The low number of students who accessed the system could be partly due to the lack of ICT and
Internet skills amongst students in Tanzania.

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Lack of Awareness of the System

Another challenge that could have limited students’ access to the system is lack of awareness among
students of the existence of the system. The college and Halotel mobile firm have been advertising this
system via social media and some selected radios in Tanzania. Due to the large population size of
students and limited advertisement budgets, it is unlikely that many students are aware of this
system.

Conclusion
The adopting and use of eLearning systems in enhancing the quality of teaching and learning at
various levels of education in Africa will continue to increase given the proliferation of mobile phones
and the Internet. With these systems continuing to generate massive amounts of new data through the
data log, it is important to help educators with tools that will help them to understand the status of
students’ learning and finding ways of helping struggling students. The use of data mining tools can
effectively utilise existing generated data in eLearning systems to provide feedback for instructors
about the efficiency of education such as the quality of students’ postings, visualisation usage
behaviors, and engagement levels.

This study aimed to demonstrate how the existing data mining tools can be used to provide important
information about students’ access in the system implemented in secondary schools in sub-Saharan
Africa. To do so, the study utilised data from a Halostudy system log to investigate students’ usage
patterns in the system implemented in secondary schools in Tanzania using WEKA and KEEL as data
mining tools. Using a total of 68,827 individual records accessed in nearly 14 moths, the study was
able to generate useful usage patterns that can help educators to make informed decisions in finding
strategies that will maximise system usage.

Generally, the usage of the system has been moderate and has been declining almost every month.
Declining usage is an important indication that the anticipated benefits may not be realised. These
findings call for immediate action in order to find ways of ensuring that users use the system. The
findings of this study have shown that data mining tools can be used to show usage patterns of
systems implemented in sub-Saharan Africa through the use of system log data. However, one
notable weakness of this study is that the quantitative data do not provide explanations of such trends
and patterns. For instance, reasons for multimedia access per subject were high for biology compared
to other subjects that could not be revealed. A mixed study would have been appropriate in
complementing the findings obtained from the quantitative data. A further study with more focus on
qualitative data is therefore recommended.

References
Barakabitze, A. A., Kitindi, E. J., Sanga, C., Kibirige, G., & Makwinya, N. (2015). Exploring students’ skills and

attitudes on effective use of ICTs: Case study of selected tanzanian public secondary schools. Universal
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Authors:
Joel S. Mtebe, is a Senior Lecturer in Computer Science and the Director of the Center for Virtual Learning
(CVL) at the University of Dar es Salaam, Tanzania. He received a B.Sc. in Computer Science and Statistics from
the University of Dar es Salaam (UDSM), Dar es Salaam, Tanzania in 2002, a Master’s of Online Education from
the University of Southern Queensland, Australia in 2004, and his doctoral degree in Interactive
Technology/Human Computer Interaction from the University of Tampere in Finland in 2014. Email:
jmtebe@gmail.com
Aron W. Kondoro, received his Master of Science degree in Information and Communication Systems Security
from the Royal Institute of Technology (KTH - Sweden) in 2012 and Bachelor of Science in Computer Science
from the University of Dar es Salaam in 2007. He is an Assistant Lecturer in the Computer Science and
Engineering (CSE) Department at the University of Dar-es-Salaam (UDSM), Tanzania. Currently, he is a PhD
student at KTH/UDSM doing research in using smart grid technologies to design and implement more efficient,
reliable and autonomous solar-driven microgrids for off-grid rural communities. His other research and
consultancy activities are focused on analysing ICT systems security and using mobile technologies to design
applications for educational and financial use-cases. Email: awkondoro@gmail.com

Cite this paper as: Mtebe, J. S., & Kondoro, A. W. (2019). Mining Students’ Data to Analyse Usage Patterns in eLearning
Systems of Secondary Schools in Tanzania. Journal of Learning for Development 6(3), 228-244.