RP-Department of Computing & Information Technology

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Hybrid Machine Learning Techniques for Comparative Opinion Mining
(IJAIDM, 2023-08) Ondara, Bernard; Waithaka, Stephen; Kandiri, John; Muchemi,Lawrence
Comparative opinion mining has lately gained traction among individuals and businesses due to its growing range of applications in brand reputation monitoring and consumer decision making among others. Past research in sub-field of opinion mining have mostly explored single-entity opinion mining models and the mining of comparative sentences suing single classifiers. Most of these studies relied on a limited number of comparative opinion labels and datasets while applying the techniques in limited domains. Consequently, the reported performances of the techniques might not be optimal in some cases like working with big data. In this study, however, we developed four hybrid machine learning techniques, with which we performed multi-class based comparative opinion mining using three datasets from different domains. From our results, the best-performing hybrid machine learning technique for comparative opinion mining using a multi-layer perceptron as the base estimator was the Multilayer Perceptron + Random Forest (MLP + RF). This technique had an average accuracy of 93.0% and an F1-score of 93.0%. These results show that our hybrid machine learning techniques could reliably be used for comparative opinion mining to support business needs like brand reputation monitoring.
The Competencies of Fashion Design Teachers in Public Institutions of Higher Learning in Nairobi County, Kenya
(IJSBAR, 2016) Isika, Juliet Kaindia; Mburugu, Keren; Nguku, Everlyn; Obere, Almadi
‘Real’ fabric draping involves the use of sample textile, fabric or cloth to make patterns or garments on a model or dress form stands manually. The technique is suitable for ready-to-wear and couture garment designs and has numerous advantages, including satisfaction with garment fit, accurate proportions of fabric division and reduced time waste. Numerous studies in Kenya have been carried out on the subject of Home Science. However, little documentation exists on ‘real’ fabric draping for design in Kenya. This paper anchors its discussion on the findings of a study that sought to assess the usage of ‘real’ fabric in draping by teachers in public institutions of higher learning and fashion designers in Nairobi County, Kenya, and assesses the competencies of fashion design teachers in Nairobi County, Kenya. It also examines the relationship between the use of ‘real’ fabric draping for design, on the one hand, and the teachers’ area of training on the other hand. The study was guided by the activity theory and pedagogic activity system structure. Employing a crosssectional survey research design, five public institutions of higher learning were purposively selected. The sample size comprised five heads of department, 32 teachers and 266 students. The data was collected using questionnaires and interview schedules. Both qualitative and quantitative data analysis techniques were used. The results revealed that very few public institutions of higher learning use ‘real’ fabric draping for design. Majority of the teachers were not trained in the area of fashion design. Chi-square analysis results yielded a fairly strong relationship between use of ‘real’ fabric draping for design and pattern development technique taught (V= 0 .646; p < 0.0001*) and sources of curriculum (V= 0.623; p < 0.0001*). Use of ‘real’ fabric draping for design had a weak association with teachers’ area of training (V = 0. 018; p < 0.006). It was concluded that the teachers area of training was not highly associated with the use of ‘real’ fabric draping. This may be due to the fact that most fashion design teachers were trained in clothing / garment design and are able to understand the technique. Pattern development technique taught and sources of curriculum and teachers’ area of training are the key issues associated with the use of ‘real’ fabric draping for design in public institutions of higher learning. This paper recommends that public institutions of higher learning should ensure that teachers engaged have the adequate skills to teach ‘real’ fabric draping for design as a practical unit. This would ensure that the students acquire pertinent skills imparted as prescribed in the curriculum.
Social Media Influence on Personal Security among the Youth in Nairobi City County, Kenya
(EANSO, 2023) Soita, Sally; Njoroge, Harrison
This study examined positive uses of social media that include warning and preventing individuals from violence resulting from negative uses of social media and user victimisation. The study was guided by Space transition theory which states that criminals are more likely to commit crimes in cyberspace more than in physical space due to anonymity and identity flexibility. The objective of the study was to determine the forms of social media use among the youths in Nairobi County. The target population were members of the Professional Criminologists Association of Kenya (PCAK). Purposive sampling was used to select 155 youth respondents from a population of 15000 youths and 145 law enforcement informant interviewees drawn from 2,000 law enforcement officers in PCAK in Nairobi County. Piloting of the questionnaire was disseminated among 30 PCAK youths Nakuru chapter. The research instruments were verified by the supervisor for content validity. Statistical Packages for Social Sciences, SPSS and Microsoft Excel software were used in data entry and descriptive statistics were used to analyse the data. Qualitative data were analysed using content analysis, coding, classification, and text inferencing. This study was significant to academic research, criminal justice practitioners and the private sector to assist in goal formulation and achievement of cyber security. The results of this research showed that the form of social media that youth mostly prefer +are WhatsApp over other social media platforms. The most preferred social media platforms by both genders were found to be WhatsApp and Twitter. It was recommended that future research could focus on the modern methods of social media as technology is dynamic. This will give direction on the contemporary forms of social media and their relationship to personal security; this, in turn, improves the security settings suitable for the users.
Critical Literature Review on Current State-of-the Art in Predicting Students’ Performance using Machine Learning Algorithm in Blended Learning Environment
(AJOEI, 2023) Ofori, Francis; Matheka, Abraham; Maina, Elizaphan
Background of the study: Predicting and analyzing the performance of the student in a blended learning environment is important to help educators identify poor performing students and improve their academic score. Meanwhile, achieving accurate predictions require selecting machine learning techniques that can produce optimum score. However, there seems to be no critical literature review on current state of art in predicting students’ performance using machine learning algorithms in blended learning environment. Methodology: This critical literature review focuses on, studies on the current state of the art in predicting students’ performance in the blended learning for past 10 years, sources of dataset used by various authors and the machined learning algorithm with high prediction accuracy. Findings: Naïve Bayes was the most frequently used algorithm for predicting students’ performance. Authors mostly used online data for their student’s performance prediction. Finally, artificial neural network was found to give higher prediction accuracy of 98.7%.
Using Educational Data Mining Techniques to Identify Profiles in Self-Regulated Learning: An Empirical Evaluation
(International Review of Research in Open and Distributed Learning, 2022) Araka, Eric; Oboko, Robert; Maina, Elizaphan; Gitonga, Rhoda
With the increased emphasis on the benefits of self-regulated learning (SRL), it is important to make use of the huge amounts of educational data generated from online learning environments to identify the appropriate educational data mining (EDM) techniques that can help explore and understand online learners’ behavioral patterns. Understanding learner behaviors helps us gain more insights into the right types of interventions that can be offered to online learners who currently receive limited support from instructors as compared to their counterparts in traditional face-to-face classrooms. In view of this, our study first identified an optimal EDM algorithm by empirically evaluating the potential of three clustering algorithms (expectation-maximization, agglomerative hierarchical, and k-means) to identify SRL profiles using trace data collected from the Open University of the UK. Results revealed that agglomerative hierarchical was the optimal algorithm, with four clusters. From the four clusters, four SRL profiles were identified: poor self-regulators, intermediate self-regulators, good self-regulators, and exemplary selfregulators. Second, through correlation analysis, our study established that there is a significant relationship between the SRL profiles and students’ final results. Based on our findings, we recommend agglomerative hierarchical as the optimal algorithm to identify SRL profiles in online learning environments. Furthermore, these profiles could provide insights on how to design a learning management system which could promote SRL, based on learner behaviors.
Determinants of Mobile Banking Adoption by Customers of Microfinance Institutions in Nairobi County in Kenya
(International Journal of Science and Research (IJSR), 2015) Wamai, John; Kandiri, John M
Mobile technology usage has had various impacts on individuals and enterprises at different levels. Several factors have been sighted by different researchers as contributing either positively or negatively to the adoption of Mobile banking technology. Banks have implemented this technology to enable them reach more customers due to its ubiquitous nature and to reduce the cost of putting up new branches in their areas of operations. For this effort to be felt and for the technology to be implemented effectively, there is need to understand the factors contributing to its adoption by the customers. This study was carried out to investigate the effects of important factors that affect adoption of mobile banking technology by customers of Microfinance Institutions in Nairobi County, Kenya. A sample of 210 customers were selected randomly and the researcher extended the Technology Acceptance Model (TAM) framework. The study found that both perceived usefulness and perceived ease of use positively correlate and affects adoption of mobile banking technology positively. On the other hand, Perceived Risk and Perceived transaction costs were found to have negative correlation with the adoption of mobile banking technology
Students’Perceived Challenges in an Online Collaborative Learning Environment: A Case of Higher Learning Institutions in Nairobi, Kenya
(\'E}rudit, 2014) Muuro, Maina Elizaphan; Wagacha, Waiganjo Peter; Oboko, Robert; Kihoro, John
Earlier forms of distance education were characterized by minimal social interaction like correspondence, television, video and radio. However, the World Wide Web (WWW) and online learning introduced the opportunity for much more social interaction, particularly among learners, and this has been further made possible through social media in Web 2.0. The increased availability of collaborative tools in Web 2.0 has made it possible to have online collaborative learning realized in Higher Learning Institutions (HLIs). However, learners can perceive the online collaborative learning process as challenging and they fail to utilize these collaborative tools effectively. Although a number of challenges have been mentioned in the literature, considerable diversity exists among countries due to diversity in infrastructure support for e-learning and learners’ background. This motivated this study to investigate components of online collaborative learning perceived as challenging by learners in HLIs in Kenya. Using a questionnaire, a survey was conducted in two public universities and two private universities to identify students’ perceived challenges in an online collaborative learning environment. Through purposive sampling the questionnaire was distributed to 210 students using e-mail and 183 students responded. Based on descriptive analysis the following five major challenges were rated as high: lack of feedback from instructors, lack of feedback from peers, lack of time to participate, slow internet connectivity, and low or no participation of other group members. There was also a relationship between the university type (private or public) with the perceived challenges which included: lack of feedback from the instructor (𝒑=0.046) and work load not shared equally among group members (𝒑=0.000). Apart from slow internet connectivity the rest of the challenges were in line with the observed challenges in the literature.These key challenges identified in this study should provide insight to educators on the areas of collaborative learning that should be improved in order to provide access to quality education that supports effective online collaborative learning in HLIs in Kenya
University Students’ Perception on the Usefulness of Learning Management System Features In Promoting Self-Regulated Learning in Online Learning
(University of Nairobi, 2021) Araka, Eric; Maina, Elizaphan; Gitonga, Rhoda; Oboko, Robert; Kihoro, John
Online learning has increasingly been adopted by most institutions of higher learning to facilitate teaching and learning as a continuum to the traditional face-to-face approach. Most of these institutions utilize Learning Management Systems which contain features that are intended to make students active participants not only by delivering learning resources to learners but also providing the environment for effective interaction in the learning process. Our examination of the literature reveals that there is limited empirical evidence that addresses how these features are being utilized by students in promoting Self-Regulated learning. To realize the usefulness of the features of Learning Management Systems in promoting Self-Regulated Learning, a structured survey was carried out among University students in Kenya. The findings reveal that the features of Learning Management Systems are underutilized by students. The qualitative results of the study illustrate that students face several challenges that obstruct them from being actively involved in online learning. There is lack of individualized feedback on students’ learning habits, lack of instructor guidance, lack of interaction with course instructors, lack of peer interaction and lack of automation tools. This study provides insights for educators and researchers on the areas of focus that can be prioritized towards offering support to students in improving their SelfRegulated learning in online learning environments.
Multiplier Design using Machine Learning Alogorithms for Energy Efficiency
(VLSI, 2023) Juma, Jane; Mdodo, R.M.; Gichoya, David
Designers primary goal is to develop the Adder cell with improved performance viz. speed, fixed rise and fall time, as it the fundamental block in VLSI design process. The dynamic logic circuits are far better than the static logic circuits because it consumes less power and speed performance also increased. But, cascading of several blocks in dynamic logic is found to be a wrong analysis. This drawback of increased complexity with mismatched cascading is overcome by using domino logic circuits. By using domino logic circuits, the reduction of noise margins and increase the speed performance of the circuit is achieved. In this paper, domino logic based Manchester carry chain adder (MCC) is designed using FinFET 18nm technology in Cadence virtuoso. It is noticed that 4-bit and an 8-bit Manchester Carry Chain Adder (MCC) using domino logic design consumes less power and reduction in the delay of the proposed circuit compared with the previous architecture. Implementation results reveal that the 4-Bit MCC Adder has delay of 79.45% less compared to the existed standard design and power consumption also reduced to 94.15%.
Customers Perception on Prior Knowledge of Technology and Its Effect on Usage of Internet Banking in Commercial Banks in Kenya
(Paper Publications, 2015) Waithaka, Stephen Titus; Muthengi, Kilembwa; Nzeveka, Joseph
Internet banking allows banks to provide information and offer services to their customers conveniently using the internet technology. However, studies have shown that customers have perceptions that impact on the uptake and continuous usage of the platform. The purpose of this study is to understand the effect of customer perceptions on usage of internet banking in commercial banks in Kenya. This study used descriptive research design while a stratified random sampling technique was used to select subjects to represent the target population which was made up of 1,837,312 customers of commercial banks within Nairobi County. An estimated 384 respondents were targeted to participate in the study. 272 questionnaires representing a 71% response rate were received and analysed. Based on the findings of the research it was concluded that customers perceptions have an effect on usage of internet banking. Prior knowledge of technology was forund to have an impediment in using internet banking by customers. Not all customers are well versed in using systems used in accessing internet banking- both software and hard ware.
The Effect of Customers Perception on Security and Privacy of Internet Banking on Its Usage in Commercial Banks in Kenya
(Paper Publications, 2015) Waithaka, Stephen Titus; Muthengi, Kilembwa; Nzeveka, Joseph
Internet banking allows banks to provide information and offer services to their customers conveniently using the internet technology. However, studies have shown that customers have perceptions that impact on the uptake and continuous usage of the platform. The purpose of this study is to understand the effect of customer perceptions on usage of internet banking in commercial banks in Kenya. This study used descriptive research design while a stratified random sampling technique was used to select subjects to represent the target population which was made up of 1,837,312 customers of commercial banks within Nairobi County. An estimated 384 respondents were targeted to participate in the study. 272 questionnaires representing a 71% response rate were received and analysed. Based on the findings of the research it was concluded that customers have perception that have an effect on usage of internet banking. Customers both users and potential, are still apprehensive about the security of internet banking transactions and privacy of their sessions while online. Due to increased phishing, on online scams and frauds perpetrated online customers are reluctant to adopt or continue using internet banking. It is the responsibility of commercial banks to sensitize their customers and assure them that it is safe to access internet banking from both a private and public network. They should provide customers with guidelines on how to safe guard their information and secure their log on credential while using both private and public network
Hospital Information Systems Capability and End-User Satisfaction in Hospitals of Nairobi County, Kenya
(IAJIST, 2018) Omune, Onyando George; Kandiri, John M.
Hospitals in Nairobi County, Kenya continue to automate their processes to improve service delivery to their clients by implementing hospital Information Systems (HIS). As studies have revealed, end user satisfaction plays an important role in information systems acceptance, and ultimate success. The study focused on HIS capability and how it affects end user satisfaction in the hospitals by use of descriptive and observations techniques. The scope of this study was hospitals in Nairobi County, Kenya with bed capacity of at least 100 and had used HIS for a period of not less than a year. Stratified sampling method was preferred for sampling of study respondents from the selected ten hospitals of Nairobi County that met inclusion criteria and simple random sampling technique, to select respondents respectively. Semi structured questionnaires were used to collect primary data from a population of 374 respondents. The data collected was analyzed quantitatively using descriptive statistics comprising of the mean, standard deviation and P-values. Statistical Package for Social Sciences (SPSS) version 22, Microsoft Office Excel 2013 and descriptive statistics were the main tools used to analyze the data. The results have shown that systems quality, information quality and service quality of HIS positively affect end user satisfaction.
Machine Learning Techniques, Features, Datasets, and Algorithm Performance Parameters for Sentiment Analysis: a Systematic Review
(COAS, 2022) Ondara, Bernard; Waithaka, Stephen; Kandiri, John
The purpose of this paper is to review various studies on current machine learning techniques used in sentiment analysis with the primary focus on finding the most suitable combinations of the techniques, datasets, data features, and algorithm performance parameters used in most applications. To accomplish this, we performed a systematic review of 24 articles published between 2013 and 2020 covering machine learning techniques for sentiment analysis. The review shows that Support Vector Machine as well as Naïve Bayes techniques are the most popular machine learning techniques; word stem and n-grams are the most extensively applied features, and the Twitter dataset is the most predominant. This review further revealed that machine learning algorithms' performance depends on many factors, including the dataset, extracted features, and size of data used. Accuracy is the most commonly used algorithm performance metric. These findings offer important information for researchers and businesses to use when selecting suitable techniques, features, and datasets for sentiment analysis for various business applications such as brand reputation monitoring.
Facilitating Group Learner Participation using Intelligent Agents in Collaborative M-Learning
(IEEE, 2018) Njenga, Stephen; Oboko, Robert; Omwenga, Elijah; Maina, Elizaphan
Most Leaning Management Systems provide a facility for online group discussions. Grouping members together does not give a surety of their participation in those online discussions. However, intelligent agents can facilitate group participation to motivate members to participate effectively in group discussions leading to enhanced levels of group knowledge construction. This paper discusses an experimental design for evaluating agent-based facilitated group learner participation for online group discussions in mobile learning environments. The experiment uses two treatment groups and one control group. We compared the levels of group knowledge construction amongst the three groups. The findings showed improved levels of group knowledge construction in the treatment groups compared to the control group. Thus, we recommend the use of intelligent agents in facilitating group participation and subsequently improving the group knowledge construction in collaborative m-learning.
Research Trends in Measurement and Intervention Tools for Self-Regulated Learning for E-Learning Environments—Systematic Review (2008–2018)
(Springer Link, 2020) Araka, Eric; Maina, Elizaphan; Gitonga, Rhoda; Oboko, Robert
For the last one decade, research in self-regulated learning (SRL) and educational psychology has proliferated. Researchers and educators have focused on how to support leaners grow their SRL skills on both face-to-face and e-learning environments. In addition, recent studies and meta-analysis have greatly contributed to the domain knowledge on the use of SRL strategies and how they contribute and boost academic performance for learners. However, there is little systematic review on the literature on the techniques and tools used to measure SRL on e-learning platforms. This review sought to outline recent advances and the trends in this area to make it more efficient for researchers to establish the empirical studies and research patterns among different studies in the field of SRL. The findings from this study are concurrent with existing empirical evidence that traditional methods designed for classroom supports are being used for measuring SRL on e-learning environments. Few studies have used learner analytics and educational data mining (EDM) techniques to measure and promote SRL strategies for learners. The paper finally points out the existing gaps with the tools presently used to measure and support SRL on learning management systems and recommends further studies on the areas of EDM which can support SRL
A Literature Review on Automatic Generation of Examinations
(COAS, 2021) Ndirangu, Peter Ndegwa; Muuro, Elizaphan Maina
The examination is a key activity in determining what the learner has gained from the study. Institutions of higher learning (IHL) perform this activity through various assessment methods (test/examination, practical, etc.). The world today is focused on automation of exam generation which is ongoing with dire need during this period of the COVID-19 pandemic when education is greatly affected, leading to embracing online learning and examination. A text/exam comprises questions and answers that focus on evaluation to determine the student’s conversant level in the area of study. Each question has a cognitive level as described by (Armstrong, 2016) in the revised Bloom’s taxonomy. Questions chosen have cognitive levels based on the level of study and standardization of the exam. There is, therefore, a need to consider the question’s cognitive level along with other factors when generating an examination by incorporating deep learning algorithms.
Learner Experience of E-learning Mode in Institutions of Higher Learning: A Case of Kenyan Universities
(IEEE, 2017) Maina, Elizaphan Muuro; Kihoro, John M.
This paper investigates learner experiences of e-learning mode in Institutions of Higher Learning (IHL) through a survey which was conducted in three public Universities. Through purposive sampling a questionnaire was distributed online to 300 students and 198 students responded. Based on descriptive analysis it was noted that students still preferred e-learning mode because of its convenience and flexibility. However, students still experienced some challenges such as inadequate lecturer facilitation, inadequate learning materials, lack of feedback from instructors, slow internet connectivity, and high internet rates. There was also significant difference between (i) gender ( =0.021), (ii) Age ( =0.030) and (iii) programme level ( =0.001) and students’ attitude towards e-learning mode. In view of these challenges students suggested that e-learning facilitators should; avail adequate quality learning materials, interact with students more frequently, introduce other modes of delivery such as U tube, Skype, and Video, increase internet bandwidth and consider introducing more courses online.
Intelligent Conversational Agent for Enhancement of Online Communication in Universities: An Overview of Kenyatta University
(COAS, 2021) Kuria, Isaac; Njoroge, Harrison
University websites and online portals are the primary means through which potential students and other stakeholders find important information about an institution. University websites are essential to these organizations’ marketing and communication efforts. In this paper, focus has been put on the need to complement these websites with the use of an AI Chatbot (UniBot) in order to serve more efficiently. This study aims at performing an extensive literature survey on intelligent conversational agents and the feasibility of applying them in enhancing online communication in universities. The study utilizes an iterative – incremental methodology to aid in design and development of UniBot, using AIML (Artificial Intelligent Markup Language) Pattern matching algorithm on the Pandorabot (AIAAS) platform, to generate high quality training data, with which, the agents Natural Language Understanding (NLU) model is trained. The study also provides for training and testing the agent using data which is acquired from Online Communication, University Website department at Kenyatta University.
Gamified Blood Donors System Based on Intelligent Agents
(OJIT, 2021) Karanja, James Mugi; Njoroge, Harrison
The population of the country of Kenya is drastically increasing thus causing the number of possible blood donors to rise. Despite this, the blood collected and stored in most blood banks is not enough to cater for the huge demand. The demand has been due to increase of number of accidents experienced in the country and the advancement in medical procedures which calls for organ transplant and blood transfusion. Even though systems have been developed which can connect the donors and recipients and location tracking, most people are dying because they don`t get this vital commodity in good time. The process of donating blood has not been enticing. There is nothing that prompts a person to donate blood. This call for developing a gamified blood donor management system based on intelligent agents so as to increase the number of donors and keep the system performance at optimal level. The project adopts Goal-Oriented Methodology in the system development process. Two agents are developed: donors’ agent and the blood admin agent. The intelligent agents help in profiles personalization thus improving the system performance. Gamification technique is implemented in the system so as to increase the traffic of blood donors interacting with the system and participating in the donation exercise. This increase the number of blood donors hence enough blood is collected to cater for the huge demand
Multi-Agent Adaptive E-Learning System Based On Learning Styles
(Center for Open Access in Science, 2021) Kivuva, Faith Ngami; Maina, Elizaphan
Most traditional e-learning system fails to provide the intelligence that a learner may require during their learning process. Different learners have different learning styles but the current elearning systems are not able to provide personalized learning. In this paper, we discuss how intelligent agents can aid learners in their learning process. Three agents have been developed namely, learner agent, information agent, and tutor agents that will be integrated into a learning management system (Moodle). Learners are provided with a personalized recommendation based on the learning styles.

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