Effectiveness of Reference Management Software in Enhancing Research Quality in Universities in Nairobi County, Kenya

PowerPoint presentation; presented at the Standing Conference of Eastern, Central and Southern African Library and Information Associations.

Evaluating the Performance of Tree-Based Predictive Models as Programme Recommenders for University Entrants in Kenya.

AbstractEnrolling for the wrong programme by university students has, to an extent, contributed to the high rates of discontinuation on academic grounds, repeat year cases, change of programme after registration, interuniversity transfers, deferments to change programme, drop out cases, suspension over exam irregularities as well as to strikes. This study focused on finding a technological solution for reducing these cases by evaluating three tree-based predictive models and recommending the most predictive model to implement as a programme recommender. Data was collected in five selected public universities in Kenya using Google Forms. The respondents were 308 translating to 308 rows of data with 36 columns. Numpy, Pandas, Matplotlib, Sklearn, Seaborn, Scipy, Plotly python analytics libraries were deployed using Jupyter Notebook for Anaconda. The cleaned and processed dataset features had categorical variables thus one-hot-encoding technique was employed. Data was split for training and testing with the random_state set to 42. Gini index criteria was implemented. The three models were evaluated on their performance from the optimally split data for training and test with a 80:20 ratio. Random Forest (RF) came out the most predictive at 99.3% followed by Gradient Boosting (XG Boost) at 90% then Decision Tree (DT) at 80.93%. The testing accuracy score for RF was 81.72%, XGBoost was at 75.72% and DT was at 76.34%. Confusion matrix criterion was implemented to evaluate the performance of the three models. The results of this study have demonstrated the high accuracy level of RF as the most predictive tree-based model for this real-world University crisis. The model is recommended for development as a system to be integrated into the KUCCPS portal. The integrated system is dubbed Programme Recommender which if launched would highly predict the best programme of study for application by university entrants

The Surge of Africa’s Digital Economy during COVID-19: Impact on the Diaspora Communities

Abstract on The Surge of Africa’s Digital Economy during COVID-19This chapter focuses on the surge in Africa’s digital economy during the COVID-19 pandemic. The author examines how digital technologies were adopted and used to enhance economic development during the pandemic. The research is based on a thorough literature review conducted using the Google Search Engine, focusing on literature that explores the impact of the COVID-19 pandemic on the digital economy in Africa. The reviewed literature demonstrates that the pandemic adversely affected economies in Africa and globally. Specifically, it significantly impacted Diaspora communities, particularly regarding their income and home remittances. Additionally, the literature highlights that digital technologies present an opportunity to transform business models and services across various sectors of different countries’ economies. In light of these findings, it is recommended that Diaspora communities adopt a strategic approach to create an enabling environment for adopting and utilizing digital technologies, thereby advancing their economic activities and providing a buffer against potential future shocks like COVID-19

Mainstreaming Digital Platforms in Curating Indigenous Knowledge for Sustainable Development in Kenya

Abstract on Mainstreaming Digital Platforms in Curating Indigenous KnowledgeThe rapid evolution of digital platforms is an opportunity for custodians of indigenous knowledge (IK) to utilise digital spaces to enhance IK for promoting sustainable development in Kenya. IK refers to skills, systems, and practices developed and used by indigenous people over generations to sustain livelihoods, manage natural resources and preserve cultural heritage. Safeguarding IK in Kenya involves recognising, respecting, and protecting the IK for posterity. Unfortunately, indigenous communities in Kenya continue to be ignored and forgotten in policy. Therefore, implementing the Sustainable Development Goals (SDGs) should include protecting IK. The objectives of this paper are to (i) describe the digital platforms currently used in Kenya to curate IK; (ii) analyse the effectiveness of digital platforms in preserving and disseminating IK; and (iii) propose recommendations for policymakers to consider when integrating digital platforms in curating IK and implementation of SDGs in Kenya. The study employed a mixed-methods approach, combining qualitative and quantitative data collection methods. Quantitative data was collected through bibliometrics analysis. Qualitative data was collected using a systematic literature review. Data was collected from Google Scholar using Harzing’s “Publish or Perish” software, analysed using Microsoft Excel, Notepad, and VOSviewer, and presented using tables, graphs, and figures. The study findings would be helpful in providing recommendations to inform policymakers on the importance of using digital platforms to curate IK for the promotion of SDGs in Kenya. The study demonstrates that digital platforms can be used to promote the sharing of knowledge and best practices amongst different indigenous communities in Kenya and for policymakers to enact and review policy frameworks on the use of digital platforms to prevent IK from potential loss or exploitation. The research is original in scope and coverage. Keywords: Innovation, community-led development, human rights, emerging trends

Salinity tolerance, growth and survival of three Artemia franciscana (Kellogg, 1906) populations under laboratory conditions

Main textIn the 1980s, Artemia franciscana from San Francisco Bay (SFB) was introduced into Kenyan saltworks, where it has colonized and established stable populations. However, little is known about its biology, particularly with respect to its parental SFB population. This study compared the salinity tolerances of Kenyan (KEN) population, their SFB pro-genitors and those of Great Salt Lake (GSL) populations. Growth and survival of these A. franciscana populations were evaluated under varying salinity levels in a laboratory set up. A. franciscana nauplii were cultured at a rate of 1 nauplii/mL in 36 Erlenmeyer flasks and fed microalgae (Chaetoceros sp.) at 1.5 × 106 cells/animal/day for 8 days. Survival was evaluated daily and survivors were fixed in individual vials with Lugol solution. The total length of each fixed A. franciscana nauplii specimen was measured under a compound microscope. All populations were susceptible to salinities greater than 100 g/L. Compared with the parental SFB population, the KEN population exhibited significantly reduced survival and growth at 140 g/L, suggesting a narrower salinity tolerance range. These findings underscore the need for further studies focusing on other physiological parameters, abiotic factors and genetic characterization to confirm whether the KEN population is experiencing ecological adaptation. This will contribute to the optimization of Artemia practices in various salinity environments as a result of climate change

Macroinvertebrate metrics and lipid profiles as potential indicators of land use influence in a high altitude tropical highland stream (Sagana River Basin, Kenya)

Research article on Macroinvertebrate metrics and lipid profilesLand use practices near river ecotones result in numerous inputs of terrestrial materials into the river ecosystem. While macroinvertebrates population metrics is widely used in monitoring water quality changes, including those induced by humans, the use of lipid profiles in these organisms to monitor influence of land use practices is rather ignored. The aim of this study was to determine lipid profile in macroinvertebrates as potential indicator of human influence in Sagana River Basin, a high altitude tropical highland stream in Kenya. Sites were selected based on differences in land use activities delineated: forest with minimal land use activities, while cropland and saw milling areas had varying degrees of land use activities, with urban areas designated as sites with most human impacts. The macroinvertebrate metrics such as occurrence, abundance, diversity, percentage of oligo chaetes and chironomids (%OC), as well as the Ephemeroptera, Plecoptera and Trichoptera (%EPT) index differed relative to land use changes. Variation in the Fatty Acids (FAs), alcohols and sterols in benthic mac roinvertebrates was related to the land use activities along the stream. Evidently, streams traversing the urban areas had higher concentrations of Phospholipid fatty acids (PLFA), while streams traversing croplands had the highest neutral lipid fatty acids (NLFA). The dominance of monounsaturated FAs (MUFAs) in forest, shortchain FAs (SCFAs) and polyunsaturated FAs (PUFAs) in croplands and longchain FAs (LCFAs) near urban areas clear indicate different sources of these materials, mainly from terrestrial sources. Individual fatty acids, alcohols and sterols profiles discerned difference in land use changes. The concentrations of SCFAs, MUFAs, LCFAs, TeoH, 27Δ5.22, 28Δ5,24, 28Δ5,22, 29Δ5,22 and 29Δ5,22 in the benthic macroinvertebrates samples were positively associated with OC%, EPT, total abundance and eH’. Meanwhile the concentration of phytol, AqOH, 27Δ5 and SCOH were associated with Simpson index. The current findings support the combination of macroinvertebrates species composition, abundance, diversity and lipids profiles to detect land use activities at the riverine scale. While implementing catchment-based river management and conservation activities should incorporate lipid analysis as a management tool. There is need for continuous monitoring of long term trends in land use activities on the changes in lipid content of the macroinvertebrate

Effectiveness of Reference Management Software in Enhancing Research Quality in Universities in Nairobi County, Kenya

Research article presented during the Proceedings of the 26th Standing Conference of Eastern, Central and Southern African Library and Information Associations (SCECSAL XXVI) held on 22nd — 26th April 2024 in Mombasa, KenyaReference management software (RMS) application is highly emphasised in academic research to improve research quality. However, studies raise concerns about their effectiveness since they have errors in functionality, language limitations, and inaccuracy of the citations and references generated. This study aimed to establish how effective RMS is in improving research quality. The study objectives were to investigate what reference management software is in use in the universities in Nairobi County, Kenya and to establish if reference management software programmes are effective in enhancing the quality of research in the selected universities. The study adopted a descriptive research design. This assisted the researchers in gathering data through a survey where an online questionnaire was administered to 18 respondents. All the universities within the County of Nairobi constituted the target population. The unit of analysis was the University Librarians because of their crucial role in promoting research quality in Kenyan Universities. In the analysis, insights were derived using a computer-based statistical package for social science (SPSS). The study revealed high satisfaction with RMS, particularly in terms of necessity, visual appeal, integration capabilities, and automatic formatting of references. While University librarians reported ease of navigation, there was limited awareness of alternative referencing approaches. Continuous institutional training programs for librarians and researchers on RMS usage, covering basic and advanced functionalities, are recommended

Attributes of Urban Greenspaces and their Influence on Visitors’ Preferences in Nairobi City County, Kenya

Abstract on Urban Greenspaces and their Influence on Visitors’ Preferences in Nairobi City County, KenyaUrban green spaces refer to land covered with vegetation such as forests, street trees, parks, gardens, and water bodies in an urban setup. In this study, we sought to investigate the attributes of urban green spaces in Nairobi County and their relationships to visitor preferences. The Garden City Model advanced by Howard (1898) guided the study. Quantitative and qualitative methods were used to collect data while a cross sectional survey designs and stratified random sampling of the green spaces was employed, based on the location within the urban core and peri-urban of Nairobi County. Four green spaces (Karura Forest, Ngong Road Forest, Nairobi Arboretum, and City Park) with diverse characteristics and attributes was selected. A sample population of 384 visitors to the green spaces were surveyed for their perceptions of the functions, use, and benefits of the green spaces. Our findings showed that attributes of green spaces including location, accessibility, security, hygiene, and infrastructure could explain the interaction between green space provision factors and the frequency of visits, time spent in the green spaces and overall satisfaction of the visitors. Green space provision should be equitable in regard to distance to residents, quality of spaces, facilities and services and should be designed to meet the needs of diverse residents. Further, they should provide services and benefits such as shade, recreation, and health which are the main attractions to the green spaces. To achieve maximum benefits for visitors, green spaces need to be safe and physically accessible to all

Salinity tolerance, growth and survival of three Artemia franciscana (Kellogg, 1906) populations under laboratory conditions

FulltextIn the 1980s, Artemia franciscana from San Francisco Bay (SFB) was introduced into Kenyan saltworks, where it has colonized and established stable populations. However, little is known about its biology, particularly with respect to its parental SFB population. This study compared the salinity tolerances of Kenyan (KEN) population, their SFB progenitors and those of Great Salt Lake (GSL) populations. Growth and survival of these A. franciscana populations were evaluated under varying salinity levels in a laboratory set up. A. franciscana nauplii were cultured at a rate of 1 nauplii/mL in 36 Erlenmeyer flasks and fed microalgae (Chaetoceros sp.) at 1.5 × 106 cells/animal/day for 8 days. Survival was evaluated daily and survivors were fixed in individual vials with Lugol solution. The total length of each fixed A. franciscana nauplii specimen was measured under a compound microscope. All populations were susceptible to salinities greater than 100 g/L. Compared with the parental SFB population, the KEN population exhibited significantly reduced survival and growth at 140 g/L, suggesting a narrower salinity tolerance range. These findings underscore the need for further studies focusing on other physiological parameters, abiotic factors and genetic characterization to confirm whether the KEN population is experiencing ecological adaptation. This will contribute to the optimization of Artemia practices in various salinity environments as a result of climate change

Integrating Artificial Intelligence Literacy in Library and Information Science Training in Kenyan Academic Institutions

Conference Proceedings ArticleWith the rapid technological advancements, Library and Information Science (LIS) programs should evolve to equip students in academic institutions with Artificial Intelligence (AI) skills and knowledge to meet the demands of the information profession. The objectives of this paper were to establish the current state of AI literacy in LIS training in academic institutions in Kenya, examine the extent to which AI literacy has been integrated into LIS curricula in academic institutions, identify the challenges and opportunities associated with the integration of AI literacy in academic institutions in Kenya, and propose critical recommendations that the management in academic institutions should consider for integrating AI literacy in LIS training in Kenya. The study employed a mixed-methods approach, combining qualitative and quantitative data collection methods. Quantitative data was collected through bibliometrics analysis, while qualitative data was collected using a systematic literature review and observation. Data was collected from Google Scholar using Harzing’s “Publish or Perish” software and academic institutional websites. It was analysed using Microsoft Excel, Notepad, and VOSviewer and presented using tables, graphs, and figures. The findings reveal that LIS professionals must possess essential skills and competencies in AI to meet the evolving needs of the job market. The study highlighted valuable practical insights and recommendations to the management in academic institutions on a comprehensive understanding of the opportunities and challenges presented by AI literacy in LIS training, offering a foundation for future research, policy development, and pedagogical innovation in the field