Browsing by Author "Akande ON"

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    Dataset to support the adoption of social media and emerging technologies for students’ continuous engagement
    (2020) Akande ON; Badmus TA; Akindele AT; Arulogun OT
    The recent advancements in ICT have made it possible for teaching and learning to be conducted outside the four walls of a University. Furthermore, the recent COVID-19 pandemic that has crippled educational activities in all nations of the world has further revealed the urgent need for academic institutions to embrace and integrate alternative modes of teaching and learning via social media platforms and emerging technologies into existing teaching tools. This article contains data collected from 850 face to face University students during the COVID-19 pandemic lockdown. An online google form was used to elicit information from the students about their awareness and intention to use these alternative modes of teaching and learning. The questions were structured using the Unified Theory of Acceptance and Use of Technology (UTAUT) model. This data article includes the questionnaire used to retrieve the data, the responses obtained in spreadsheet format, the charts generated from the responses received, the Statistical Package of the Social Sciences (SPSS) file, the descriptive statistics, and reliability analysis computed for all the UTAUT variables. The dataset will enhance understanding of how face to face students use social media platforms and how these platforms could be used to engage the students outside their classroom activities. Also, the dataset exposes how familiar face to face University students are to these emerging teaching and learning technologies. The challenges that could inhibit the adoption of these technologies were also revealed.
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    Dataset to support the adoption of social media and emerging technologies for students’ continuous engagement
    (2020) Akande ON; Badmus TA; Akindele AT; Arulogun OT
    The recent advancements in ICT have made it possible for teaching and learning to be conducted outside the four walls of a University. Furthermore, the recent COVID-19 pandemic that has crippled educational activities in all nations of the world has further revealed the urgent need for academic institutions to embrace and integrate alternative modes of teaching and learning via social media platforms and emerging technologies into existing teaching tools. This article contains data collected from 850 face to face University students during the COVID-19 pandemic lockdown. An online google form was used to elicit information from the students about their awareness and intention to use these alternative modes of teaching and learning. The questions were structured using the Unified Theory of Acceptance and Use of Technology (UTAUT) model. This data article includes the questionnaire used to retrieve the data, the responses obtained in spreadsheet format, the charts generated from the responses received, the Statistical Package of the Social Sciences (SPSS) file, the descriptive statistics, and reliability analysis computed for all the UTAUT variables. The dataset will enhance understanding of how face to face students use social media platforms and how these platforms could be used to engage the students outside their classroom activities. Also, the dataset exposes how familiar face to face University students are to these emerging teaching and learning technologies. The challenges that could inhibit the adoption of these technologies were also revealed.
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    Development of a Real Time Smishing Detection Mobile Application using Rule Based Techniques
    (2022) Akande ON; Akande HB; Kayode AA; Adeyinka AA; Olaiya F; Oluwadara G
    The introduction of alternative messaging platforms on mobile devices have not been able to phase off Short Messaging Service (SMS) as the most widely used means of textual communication. Over the decades, SMS has remained the most responsive way of communication that has been embraced by individuals and organizations in passing information across to their intended recipients. However, hackers have been employing this tool as a way to deceive the gullible into divulging sensitive information about their financial dealings as well as gain access to their mobile devices. A lot of innocent but ignorant individuals have become victims of this smishing acts and have lost huge sum of money as a result. Though existing research have extensively proposed and implemented different techniques for detecting and separating spam SMS from ham SMS, a mobile application that uses a rule-based RIPPER and C4.5 classifiers in detecting smishing acts is proposed. The rule-based classifiers were used to formulate rules used in detecting and separating spam from ham while a mobile application was developed to use the rule-based model in smishing detection. An Application Programming Interface (API) was designed to intercept incoming SMS, forward them to the rule-based model for analysis and then relay the results to the user via the developed mobile application. The user then decides to either retain or discard the SMS.
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    House Price Prediction using Random Forest Machine Learning Technique
    (2022) Adetunji AB; Akande ON; Ajala FA; Oyewo O; Akande YF; Oluwadara G
    Predicting a price variance rather than a specific value is more realistic and attractive in many real-world applications. Price prediction can be thought of as a classification issue in this situation. However, the House Price Index (HPI) is a common tool for estimating the inconsistencies of house prices. Since housing prices are closely correlated with other factors such as location, city, and population, predicting individual housing prices needs information other than HPI. The HPI is a repeat-sale index that tracks average price shifts in repeat transactions or refinancing of the same assets. Therefore, HPI is ineffective at predicting the price of a single house because it is a rough predictor based on all transactions. This study explores the use of Random Forest machine learning technique for house price prediction. UCI Machine learning repository Boston housing dataset with 506 entries and 14 features were used to evaluate the performance of the proposed prediction model. A comparison of the predicted and actual prices predicted revealed that the model had an acceptable predicted value when compared to the actual values with an error margin of ±5.
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    Survey dataset on open and distance learning students’ intention to use social media and emerging technologies for online facilitation
    (2020) Arulogun OT; Akande ON; Akindele AT; Badmus TA
    Open and Distance Learning (ODL) students rely majorly on the use of Information, Communication and Technology (ICT) tools for online facilitation and other activities supporting learning. With emphasis on ODL students of Ladoke Akintola University of Technology (LAUTECH), Oyo Sta te, Nigeria; Moodle Learning Management System (LMS) has being the major medium for online facilitation for the past 5 years. Therefore, this data article presents a survey dataset that was administered to LAUTECH ODL students with a view to assess their readiness to accept and use alternative social media platforms and emerging technologies for online facilitation. The data article also includes questionnaire instrument administered via google form, 900 responses received in spreadsheet formats, chats generated from the responses, the Statistical Package of the Social Sciences (SPSS) file, the descriptive and reliability statistics for all the variables. Authors believe that the dataset will guide policy makers on the choice of social media and emerging technologies to be adopted as a facilitation tool for ODL students. It will also reveal the challenges that could militate against the willingness to use these supplementary modes of learning from students’ perspectives.
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    Survey dataset on open and distance learning students’ intention to use social media and emerging technologies for online facilitation
    (2020) Arulogun OT; Akande ON; Akindele AT; Badmus TA
    Open and Distance Learning (ODL) students rely majorly on the use of Information, Communication and Technology (ICT) tools for online facilitation and other activities supporting learning. With emphasis on ODL students of Ladoke Akintola University of Technology (LAUTECH), Oyo Sta te, Nigeria; Moodle Learning Management System (LMS) has being the major medium for online facilitation for the past 5 years. Therefore, this data article presents a survey dataset that was administered to LAUTECH ODL students with a view to assess their readiness to accept and use alternative social media platforms and emerging technologies for online facilitation. The data article also includes questionnaire instrument administered via google form, 900 responses received in spreadsheet formats, chats generated from the responses, the Statistical Package of the Social Sciences (SPSS) file, the descriptive and reliability statistics for all the variables. Authors believe that the dataset will guide policy makers on the choice of social media and emerging technologies to be adopted as a facilitation tool for ODL students. It will also reveal the challenges that could militate against the willingness to use these supplementary modes of learning from students’ perspectives.