Department of Information Technology
https://repository.maseno.ac.ke/handle/123456789/3432
2024-03-29T13:29:29ZStrengthening Online Education Approaches in Institutions of Higher Learning
https://repository.maseno.ac.ke/handle/123456789/6047
Strengthening Online Education Approaches in Institutions of Higher Learning
Adhiambo, Grace Were; Okelo, Kevin Odhiambo; Obat, Rosemary Akech
Online, distance, and eLearning (ODeL) continue to gain recognition as a mandatory component of delivery of education in institutions of higher learning (IHL) around the world following the outbreak of coronavirus disease (COVID-19). This paradigm shift is informed by the need to ensure uninterrupted, valuable, and safe learning experiences for learners during the pandemic. However, governments ordered the closure of schools and colleges following the declaration of COVID-19 as a world pandemic by the World Health Organization (WHO). A report by United Nations Educational, Scientific and Cultural Organization revealed that there was a significant loss of schooling time following the closure of educational facilities which affected over 1.5 billion learners in 194 nations globally. This study explored the use of online approaches to intensify online learning efficacy in IHL. Data collection was conducted using qualitative methods and data analysis done using themes and sub-themes. Findings from this study indicate that students’ engagements on discussion forums are consistent with collaborative learning. Results further support the view that regular, prompt, and meaningful feedback is critical in promoting constructive learning and reflection among students. Based on the findings of this study, practical implications are discussed for stakeholders interested in establishing and strengthening effective delivery of online learning content to enhance students’ learning experiences.
The article can be accessed in full via:https://www.emerald.com/insight/content/doi/10.1108/S2055-364120230000049003/full/htm
2023-05-15T00:00:00ZPolitiKweli: A Swahili-English Code-switched Twitter Political Misinformation Classification Dataset
https://repository.maseno.ac.ke/handle/123456789/6046
PolitiKweli: A Swahili-English Code-switched Twitter Political Misinformation Classification Dataset
Amol, Cynthia Jayne; Awuor, Lilian Diana Wanzare
In the age of freedom of speech, users of the social media platform Twitter post millions of messages per day. These messages are not always fact-checked resulting in misinformation which is false or misleading news. Misinformation classification involves identifying and classifying text as either false or fact by comparing the text against fact-checked news. On political matters, misinformation online can result in mistrust of political figures, polarization of communities and violence offline. Existing studies mostly address misinformation detection for messages written in a single language such as English. Among most bilingual or multilingual user groups in countries like Kenya, the use of Swahili-English code-switching and code-mixing is a common practice in informal text-based communication such as messaging on social media platforms like Twitter. There is therefore need for more research in low-resource languages such as Swahili. The PolitiKweli dataset introduced by this study, which a novel Swahili-English misinformation classification dataset, contains 6,345 Swahili-English texts, 22,957 English texts and 211 Swahili texts. The texts are labelled as fake, fact or neutral as compared to a fact-checked dataset also created for this study. The dataset curation process including data collection, processing and annotation are explained. Challenges during annotation are also discussed. The result of experiments conducted using a pretrained language model prove the dataset’s usefulness in training Swahili-English code-switched misinformation classification models.
2023-08-30T00:00:00ZEnhancing students’ biology learning by using augmented reality as a learning supplement
https://repository.maseno.ac.ke/handle/123456789/6036
Enhancing students’ biology learning by using augmented reality as a learning supplement
Weng, Cathy; Otanga, Sarah; Christianto, Samuel Michael; Ju-Chun Chu, Regina
The purpose of this study was to investigate the effects of augmented reality (AR) technology on students’ learning outcomes (measured according to Bloom’s cognitive levels) and attitude toward biology. The print book was redesigned by integrating a form of AR into it. A quasi-experimental pretest and posttest designs were used to test the effectiveness of the developed book on learning outcomes and attitude toward biology. In addition, the students’ opinions about the AR technology and the redesigned book were collected. In all, 68 ninth-grade students participated in the study. They were divided into the experimental group, who used the print book and the AR technology as a learning supplement, and the control group, who used the print book only. The results indicated that using AR technology may have the potential to enhance students’ learning outcomes at the analyzing level and their learning attitudes toward biology. The students mentioned that AR could be effective in terms of enhancing their biology learning.
The article can be accessed in full via:https://doi.org/10.1177/0735633119884213
2020-07-01T00:00:00ZEffects of tangrams on learning engagement and achievement: Case of preschool learners
https://repository.maseno.ac.ke/handle/123456789/6035
Effects of tangrams on learning engagement and achievement: Case of preschool learners
Weng, Cathy; Otanga, Sarah; Weng, Apollo; Tran, Khanh Nguyen. Phuong
The purpose of this research was to compare the effectiveness of physical and virtual tangrams on preschool children's learning engagement and achievement. Children listened to an e‐storybook narration and solved puzzles individually. The experimental group (N = 31) completed puzzles embedded in the e‐storybook using virtual tangrams, while the control group (N = 30) completed the same puzzles using physical tangrams on outlines drawn on a paper. Results indicated that the experimental group had significantly higher overall engagement than the control group. The experimental group had significantly higher learning achievement (time taken to complete outlines) when using virtual tangrams. It is hoped that the study will be beneficial to classrooms concerning how to use tangrams in teaching and learning and to instructional designers on how to design an e‐storybook for young readers.
2020-08-01T00:00:00ZFramework for Technology-Enriched Active Class Learning of Physics in Secondary Schools in Kenya
https://repository.maseno.ac.ke/handle/123456789/6034
Framework for Technology-Enriched Active Class Learning of Physics in Secondary Schools in Kenya
Abenga, Elizabeth Sarange. Bosire; Okono, Elijah Owuor; Awuor, Mzee; Otanga, Sarah
Active learning transforms the learning process and activities from tutor focused to learner-cantered and is driven by the learner's learning ability. In other words, active learning provides an opportunity for self-directed learning that enables the learners to engage with the learning materials at personal level and pace. Thus, this chapter argues that active learning can provide equal learning opportunity for every single learner irrespective of the differences in their personality traits that would otherwise affect how they learn. Hence, this chapter proposes a framework for technology-enriched active learning for young learners that provides a personalized learning that deviates from the traditional “fit-for-all” classroom setups that tends to favour only the extrovert students. The proposed framework leverages advancement in technology such as personal learning network, virtual physics labs, massive open online courses, and crowd-sourced expert opinions to provide the learners with just-in-time active learning opportunity.
2021-01-01T00:00:00ZPhonemic Representation and Transcription for Speech to Text Applications for Under-resourced Indigenous African Languages: The Case of Kiswahili
https://repository.maseno.ac.ke/handle/123456789/5530
Phonemic Representation and Transcription for Speech to Text Applications for Under-resourced Indigenous African Languages: The Case of Kiswahili
Awino Ebbie, Wanzare Lilian, Muchemi Lawrence, Wanjawa Barack, Ombui Edward, Indede Florence, Owen , Okal Benard
Building automatic speech recognition (ASR) systems is a challenging task, especially for underresourced languages that need to construct corpora nearly from scratch and lack sufficient training
data. It has emerged that several African indigenous languages, including Kiswahili, are technologically
under-resourced. ASR systems are crucial, particularly for the hearing-impaired persons who can
benefit from having transcripts in their native languages. However, the absence of transcribed speech
datasets has complicated efforts to develop ASR models for these indigenous languages. This paper
explores the transcription process and the development of a Kiswahili speech corpus, which includes
both read-out texts and spontaneous speech data from native Kiswahili speakers. The study also
discusses the vowels and consonants in Kiswahili and provides an updated Kiswahili phoneme
dictionary for the ASR model that was created using the CMU Sphinx speech recognition toolbox, an
open-source speech recognition toolkit. The ASR model was trained using an extended phonetic set
that yielded a WER and SER of 18.87% and 49.5%, respectively, an improved performance than
previous similar research for under-resourced languages.
https://arxiv.org/abs/2210.16537
2022-01-01T00:00:00ZKenSwQuAD – A Question Answering Dataset for Swahili Low Resource Language
https://repository.maseno.ac.ke/handle/123456789/5394
KenSwQuAD – A Question Answering Dataset for Swahili Low Resource Language
Wanjawa, Barack; Wanzare, Lilian ; Indede, Florence ; McOnyango, Owen ; Muchemi, Lawrence ; Ombui, Edward ;
This research developed a Kencorpus Swahili Question Answering Dataset KenSwQuAD from
raw data of Swahili language, which is a low resource language predominantly spoken in
Eastern African and also has speakers in other parts of the world. Question Answering
datasets are important for machine comprehension of natural language processing tasks such
as internet search and dialog systems. However, before such machine learning systems can
perform these tasks, they need training data such as the gold standard Question Answering
(QA) set that is developed in this research. The research engaged annotators to formulate
question answer pairs from Swahili texts that had been collected by the Kencorpus project, a
Kenyan languages corpus that collected data from three Kenyan languages. The total Swahili
data collection had 2,585 texts, out of which we annotated 1,445 story texts with at least 5
QA pairs each, resulting into a final dataset of 7,526 QA pairs. A quality assurance set of 12.5%
of the annotated texts was subjected to re-evaluation by different annotators who confirmed
that the QA pairs were all correctly annotated. A proof of concept on applying the set to
machine learning on the question answering task confirmed that the dataset can be used for
such practical tasks. The research therefore developed KenSwQuAD, a question-answer
dataset for Swahili that is useful to the natural language processing community who need
training and gold standard sets for their machine learning applications. The research also
contributed to the resourcing of the Swahili language which is important for communication
around the globe. Updating this set and providing similar sets for other low resource
languages is an important research area that is worthy of further research.
https://arxiv.org/ftp/arxiv/papers/2205/2205.02364.pdf
2022-01-01T00:00:00ZKencorpus: A Kenyan Language Corpus of Swahili, Dholuo and Luhya for Natural Language Processing Tasks
https://repository.maseno.ac.ke/handle/123456789/5393
Kencorpus: A Kenyan Language Corpus of Swahili, Dholuo and Luhya for Natural Language Processing Tasks
Barack Wanjawa, Lilian Wanzare, Florence Indede, Owen McOnyango, Edward Ombui, Lawrence Muchemi
Indigenous African languages are categorized as under-served in Artificial Intelligence and suffer poor digital inclusivity and information access. The challenge has been how to use machine learning and deep learning models without the requisite data. Kencorpus is a Kenyan Language corpus that intends to bridge the gap on how to collect, and store text and speech data that is good enough to enable data-driven solutions in applications such as machine translation, question answering and transcription in multilingual communities. Kencorpus is a corpus (text and speech) for three languages predominantly spoken in Kenya: Swahili, Dholuo and Luhya (dialects Lumarachi, Lulogooli and Lubukusu). This corpus intends to fill the gap of developing a dataset that can be used for Natural Language Processing and Machine Learning tasks for low-resource languages. Each of these languages contributed text and speech data for the language corpus. Data collection was done by researchers from communities, schools and collaborating partners (media, publishers). Kencorpus has a collection of 5,594 items, being 4,442 texts (5.6million words) and 1,152 speech files (177hrs). Based on this data, other datasets were also developed e.g POS tagging sets for Dholuo and Luhya (50,000 and 93,000 words tagged respectively), Question-Answer pairs from Swahili texts (7,537 QA pairs) and Translation of texts into Swahili (12,400 sentences). The datasets are useful for machine learning tasks such as text processing, annotation and translation. The project also undertook proof of concept systems in speech to text and machine learning for QA task, with initial results confirming the usability of the Kencorpus to the machine learning community. Kencorpus is the first such corpus of its kind for these low resource languages and forms a basis of learning and sharing experiences for similar works.
https://arxiv.org/ftp/arxiv/papers/2208/2208.12081.pdf
2022-01-01T00:00:00ZProposing Parameters for Evaluating Sustainability of mHealth Systems in Developing Countries.
https://repository.maseno.ac.ke/handle/123456789/4993
Proposing Parameters for Evaluating Sustainability of mHealth Systems in Developing Countries.
Muhambe, Titus Mukisa, Ochieng, Daniel Orwa, Wagacha, Peter Waiganjo
The exponential rise in global healthcare challenges; the rise in morbidity and mortality, especially in developing countries
have compelled stakeholders to explore alternative ways of overcoming the crisis. Guided by the recommendation of
WHO (2013), efforts have been directed towards prevention, response and strengthening of the existing healthcare
systems. There have also been efforts to explore the potential of mobile technology towards healthcare provision, with
numerous mHealth projects being reported across the developing world. Reports indicate that a significant number of
these solutions have failed before realizing the primary goals, pointing to possible mHealth sustainability challenges. The
study explored literature covering global health challenges, use of mobile technology healthcare solutions in developing
countries, as well literature covering evaluating technology sustainability. Through the review, key factors that influence
sustainability of technology were identified. A cross-sectional survey using questionnaires and a qualitative exploratory
study using interviews and Focused Group Discussion, targeting mHealth stakeholders were used to map and
contextualize the identified sustainability factors to the developing country context. The identified factors were categorized
into three broad categories; Individual factors; User Satisfactions, Access to system, and User Support, Technological
Factors; System Quality, System Scalability, Technology Sustainability, Technology Relevance and System
Interoperability and Management Factors; mHealth Ownership and Net Benefits (Return on Investment). The paper
identifies challenges in the sustainability of mHealth systems in developing countries; using Kenya health sector as a case
and proposes the sustainability evaluation parameters for mHealth systems in developing countries.
2018-01-01T00:00:00ZChallenges facing digitization projects in kenya: Case of implementation of national land information management system
https://repository.maseno.ac.ke/handle/123456789/4786
Challenges facing digitization projects in kenya: Case of implementation of national land information management system
Joseph Wang’ondu, Martin Okode Opiyo, Kariuki,Winifred N. Karugu,
Purpose: Technology has been the avenues used to bridge various challenges public service
delivery. The government of Kenya has invested heavily in land digitization process in order to
eradicate the perennial challenges in land management, and promote a conducive economic
environment for land transactions and management. The general objective of the study was to
establish the influence of challenges facing the implementation of land digitization projects of
the Kenya, with emphasis on the Ministry of Lands, Housing and Urban Development NLIMS
project.
Methodology: A descriptive survey research design, mixture of both qualitative and quantitative
research approach was adopted. A target population of 139 staff, management and vendors of
NLIMS project at all land registries implementing NLIMS project were sampled. Collected
primary data was analysed using qualitative method using descriptive and inferential statistical.
Results: A significance strong positive correlation between; governance, budgetary support and
ICT infrastructure; and NLIMS project implementation was found. In addition, a unit change in
governance, budgetary support and ICT infrastructure contributes significant factor change in
NLIMS project implementation. The study concludes that governance, budgetary support and
ICT infrastructure are the main challenges facing NLIMS project implements due to significant
influence, and Stakeholders involvement is not a challenge at implementation phase.
Unique Contribution to Theory, Policy and Practice: The study recommends that the
government should conduct training on leadership and change management to project managers
and leadership or project team in general. The government should further strengthen its
institutional framework to reduce the effect of leadership changes within its institutions and
ensure ongoing projects are not affected. The government should avail more funds towards
NLIMS project
https://www.iprjb.org/journals/index.php/IJTS/article/view/768
2018-01-01T00:00:00Z