https://repository.maseno.ac.ke/handle/123456789/3432 2026-05-15T12:08:31Z 2026-05-15T12:08:31Z Onderi, Peter Omae Oginda, Moses https://repository.maseno.ac.ke/handle/123456789/6187 2024-11-05T08:44:58Z 2024-01-01T00:00:00Z

Impact of Social Media on Student Wellbeing in Kenyan Universities Onderi, Peter Omae; Oginda, Moses Students who spend much of their time on social media are likely to struggle with time management and become less productive in their studies due to distractions by constant alerts, endless scrolling feeds, and the appeal of viral material. The chapter proposed to discuss, social networking sites, academic performance, social opportunities, and challenges. The findings were that social media helps university students get necessary information for their academic achievement, it creates stress, and it exposes them to cyberbullying, is addictive, causes sleep disorders and anxiety, and can help them make money online. The study recommended that the social media use at university should be restricted to academic use only and here should be provide adequate Wi-Fi hotspots within the universities. Source title: Student Well-Being in Higher Education Institutions.(Book). DOI: 10.4018/979-8-3693-4417-0.ch013.

2024-01-01T00:00:00Z Adhiambo, Grace Were Okelo, Kevin Odhiambo Obat, Rosemary Akech https://repository.maseno.ac.ke/handle/123456789/6047 2024-03-18T17:07:53Z 2023-05-15T00:00:00Z

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

2023-05-15T00:00:00Z Amol, Cynthia Jayne Awuor, Lilian Diana Wanzare https://repository.maseno.ac.ke/handle/123456789/6046 2024-03-18T16:24:07Z 2023-08-30T00:00:00Z

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.

2023-08-30T00:00:00Z Weng, Cathy Otanga, Sarah Christianto, Samuel Michael Ju-Chun Chu, Regina https://repository.maseno.ac.ke/handle/123456789/6036 2024-03-16T07:27:04Z 2020-07-01T00:00:00Z

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

2020-07-01T00:00:00Z Weng, Cathy Otanga, Sarah Weng, Apollo Tran, Khanh Nguyen. Phuong https://repository.maseno.ac.ke/handle/123456789/6035 2024-03-16T07:16:24Z 2020-08-01T00:00:00Z

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.

2020-08-01T00:00:00Z Abenga, Elizabeth Sarange. Bosire Okono, Elijah Owuor Awuor, Mzee Otanga, Sarah https://repository.maseno.ac.ke/handle/123456789/6034 2024-03-16T07:10:29Z 2021-01-01T00:00:00Z

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.

2021-01-01T00:00:00Z Awino Ebbie, Wanzare Lilian, Muchemi Lawrence, Wanjawa Barack, Ombui Edward, Indede Florence, Owen , Okal Benard https://repository.maseno.ac.ke/handle/123456789/5530 2022-12-06T17:22:48Z 2022-01-01T00:00:00Z

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

2022-01-01T00:00:00Z Wanjawa, Barack; Wanzare, Lilian ; Indede, Florence ; McOnyango, Owen ; Muchemi, Lawrence ; Ombui, Edward ; https://repository.maseno.ac.ke/handle/123456789/5394 2022-10-15T12:34:50Z 2022-01-01T00:00:00Z

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

2022-01-01T00:00:00Z Barack Wanjawa, Lilian Wanzare, Florence Indede, Owen McOnyango, Edward Ombui, Lawrence Muchemi https://repository.maseno.ac.ke/handle/123456789/5393 2022-10-13T16:23:53Z 2022-01-01T00:00:00Z

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

2022-01-01T00:00:00Z Muhambe, Titus Mukisa, Ochieng, Daniel Orwa, Wagacha, Peter Waiganjo https://repository.maseno.ac.ke/handle/123456789/4993 2022-02-17T08:37:05Z 2018-01-01T00:00:00Z

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.

2018-01-01T00:00:00Z