scholarly journals Data Set on Work Routine Management among Academic Staff in Selected Nigerian University: The Eustress Perspective

Data in Brief ◽  
2021 ◽  
pp. 107621
Author(s):  
Adeshola Peter ◽  
Anthonia Adeniji ◽  
Kehinde Oladele ◽  
Fred Peter ◽  
Henry Inegbedion ◽  
...  
Keyword(s):  
Academic Staff ◽  
Data Set ◽  
Work Routine ◽  
Nigerian University

scholarly journals Data set on the effect of training and development on creativity of academic staff in a selected Nigerian university

Data in Brief ◽  
2018 ◽  
Vol 18 ◽  
pp. 399-403 ◽  
Author(s):  
Maxwell Olokundun ◽  
Hezekiah Falola ◽  
Odunayo Salau ◽  
Stephen ibidunni ◽  
Fred Peter ◽  
...  
Keyword(s):  
Academic Staff ◽  
Training And Development ◽  
Data Set ◽  
Nigerian University

Professional community and student engagement in higher education: rethinking the contributions of professional staff

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yaw Owusu-Agyeman ◽  
Enna Moroeroe
Keyword(s):  
Higher Education ◽  
Student Engagement ◽  
Teaching And Learning ◽  
Academic Staff ◽  
Professional Community ◽  
Professional Staff ◽  
Data Set ◽  
Content Type ◽  
Service Culture ◽  
The University

PurposeScholarly studies on student engagement are mostly focused on the perceptions of students and academic staff of higher education institutions (HEIs) with a few studies concentrating on the perspectives of professional staff. To address this knowledge gap, this paper aims to examine how professional staff who are members of a professional community perceive their contributions to enhancing student engagement in a university.Design/methodology/approachData for the current study were gathered using semi-structured face-to-face interviews among 41 professional staff who were purposively sampled from a public university in South Africa. The data gathered were analysed using thematic analysis that involved a process of identifying, analysing, organising, describing and reporting the themes that emerged from the data set.FindingsAn analysis of the narrative data revealed that when professional staff provide students with prompt feedback, support the development of their social and cultural capital and provide professional services in the area of teaching and learning, they foster student engagement in the university. However, the results showed that poor communication flow and delays in addressing students’ concerns could lead to student disengagement. The study further argues that through continuous interaction and shared norms and values among members of a professional community, a service culture can be developed to address possible professional knowledge and skills gaps that constrain quality service delivery.Originality/valueThe current paper contributes to the scholarly discourse on student engagement and professional community by showing that a service culture of engagement is developed among professional staff when they share ideas, collaborate and build competencies to enhance student engagement. Furthermore, the collaboration between professional staff and academics is important to addressing the academic issues that confront students in the university.

scholarly journals User evaluation of a task for shortlisting papers from researcher’s reading list for citing in manuscripts

2017 ◽  
Vol 69 (6) ◽  
pp. 740-760
Author(s):  
Aravind Sesagiri Raamkumar ◽  
Schubert Foo ◽  
Natalie Pang
Keyword(s):  
Academic Staff ◽  
Evaluation Study ◽  
Manuscript Preparation ◽  
Prototype System ◽  
User Evaluation ◽  
Online Questionnaire ◽  
Data Set ◽  
Content Type ◽  
Reading List ◽  
Search Tasks

Purpose Although many interventional approaches have been proposed to address the apparent gap between novices and experts for literature review (LR) search tasks, there have been very few approaches proposed for manuscript preparation (MP) related tasks. The purpose of this paper is to describe a task and an incumbent technique for shortlisting important and unique papers from the reading list (RL) of researchers, meant for citation in a manuscript. Design/methodology/approach A user evaluation study was conducted on the prototype system which was built for supporting the shortlisting papers (SP) task along with two other LR search tasks. A total of 119 researchers who had experience in authoring research papers participated in this study. An online questionnaire was provided to the participants for evaluating the task. Both quantitative and qualitative analyses were performed on the collected evaluation data. Findings Graduate research students prefer this task more than research and academic staff. The evaluation measures relevance, usefulness and certainty were identified as predictors for the output quality measure “good list”. The shortlisting feature and information cues were the preferred aspects while limited data set and rote steps in the study were ascertained as critical aspects from the qualitative feedback of the participants. Originality/value Findings point out that researchers are clearly interested in this novel task of SP from the final RL prepared during LR. This has implications for digital library, academic databases and reference management software where this task can be included to benefit researchers at the manuscript preparatory stage of the research lifecycle.

An Application of the Data Envelopment Analysis Method to Evaluate the Performance of Academic Departments within A Higher Education Institution

2016 ◽  
pp. 71-87 ◽  
Author(s):  
Oanh Nguyen Hoang ◽  
Ngoc Nguyen Hong
Keyword(s):  
Data Envelopment Analysis ◽  
Research Output ◽  
Academic Staff ◽  
Education Institution ◽  
Academic Departments ◽  
Productivity Change ◽  
Data Envelopment ◽  
Analysis Method ◽  
Data Set ◽  
Using Data

This paper aims to evaluate the efficiency or the productivity of academic departments within a university using Data Envelopment Analysis. As an illustrative example, we investigate the performance of 57 departments of National Economics University (NEU) for three years, from 2013 to 2015. The data set consists of one input variable, which is the number of academic staff, and three output variables in which the number of research hours is considered as research output and the number of graduates and teaching load are defined as teaching outputs. Particularly, the output-oriented CCR, BCC, and SBM model under both the CRS and VRS assumptions are applied in order to determine accurate degrees of efficiency of individual departments and directions for performance improvement for less efficient ones. The output-oriented radial Malmquist DEA model is also employed to make a comparative analysis of the productivity change of the departments over the period. The results reveal some clear policy-making implications for departments to adjust their development plan in an appropriate way.

scholarly journals Student’s Success Prediction Model Based on Artificial Neural Networks (ANN) and A Combination of Feature Selection Methods

2019 ◽  
Vol 54 (3) ◽  
Author(s):  
Alaa Khalaf Hamoud ◽  
Aqeel Majeed Humadi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Feature Selection ◽  
Computer Science ◽  
Academic Staff ◽  
Educational Data Mining ◽  
Model Performance ◽  
Educational Models ◽  
Data Set ◽  
Artificial Neural

The improvements in educational data mining (EDM) and machine learning motivated the academic staff to implement educational models to predict the performance of students and find the factors that increase their success. EDM faced many approaches for classifying, analyzing and predicting a student’s academic performance. This paper presents a model of prediction based on an artificial neural network (ANN) by implementing feature selection (FS). A questionnaire is built to collect students’ answers using LimeSurvey and google forms. The questionnaire holds a combination of 61 questions that cover many fields such as sports, health, residence, academic activities, social and managerial information. 161 students participated in the survey from two departments (Computer Science Department and Computer Information Systems Department), college of Computer Science and Information Technology, University of Basra. The data set is combined from two sources applications and is pre-processed by removing the uncompleted answers to produce 151 answers used in the model. Apart from the model, the FS approach is implemented to find the top correlated questions that affect the final class (Grade). The aim of FS is to eliminate the unimportant questions and find those which are important, besides improving the accuracy of the model. A combination of Four FS methods (Info Gain, Correlation, SVM and PCA) are tested and the average rank of these algorithms is obtained to find the top 30 questions out of 61 questions of the questionnaire. Artificial Neural Network is implemented to predict the grade (Pass (P) or Failed (F)). The model performance is compared with three previous models to prove its optimality.

Knowledge Sharing Practices Among Non-Academic Staff in a Nigerian University

2021 ◽  
pp. 483-497
Author(s):  
Funmilola Olubunmi Omotayo ◽  
Hafsat Titilade Abdul-Rahman
Keyword(s):  
Knowledge Sharing ◽  
Information And Communication Technologies ◽  
Explicit Knowledge ◽  
Academic Staff ◽  
Communication Technologies ◽  
Face To Face ◽  
Information And Communication ◽  
And Performance ◽  
Nigerian University ◽  
The University

Knowledge sharing, an important part of knowledge management, has particularly been regarded as an important way of increasing competitiveness and performance of organisations. This study investigated knowledge sharing practices among non-academic staff at the University of Ibadan, Nigeria. Descriptive research designed was adopted. Findings reveal that the staff shared knowledge among themselves, and majority had understanding of, and good disposition to, knowledge sharing. The study found that the staff shared both tacit and explicit knowledge, but majorly tacit, and mainly through face-to-face interactions. The major knowledge sharing enabler is improved productivity, while lack of time is the major knowledge sharing barrier. The study concludes that there is a good knowledge sharing practices among the staff. However, there is need for the university to promote more collaboration and knowledge sharing practices among the staff by providing enabling environment for knowledge sharing, in addition to provision of adequate information and communication technologies.

What role for libraries in learning analytics?

2016 ◽  
Vol 17 (2) ◽  
pp. 203-210 ◽  
Author(s):  
Margie Jantti ◽  
Jennifer Heath
Keyword(s):  
Real Time ◽  
Data Warehouse ◽  
Learning Analytics ◽  
Academic Staff ◽  
Student Support ◽  
Distinct Advantage ◽  
Data Sets ◽  
Data Set ◽  
Content Type ◽  
The University

Purpose – The purpose of this paper is to provide an overview of the development of an institution wide approach to learning analytics at the University of Wollongong (UOW) and the inclusion of library data drawn from the Library Cube. Design/methodology/approach – The Student Support and Education Analytics team at UOW is tasked with creating policy, frameworks and infrastructure for the systematic capture, mapping and analysis of data from the across the university. The initial data set includes: log file data from Moodle sites, Library Cube, student administration data, tutorials and student support service usage data. Using the learning analytics data warehouse UOW is developing new models for analysis and visualisation with a focus on the provision of near real-time data to academic staff and students to optimise learning opportunities. Findings – The distinct advantage of the learning analytics model is that the selected data sets are updated weekly, enabling near real-time monitoring and intervention where required. Inclusion of library data with the other often disparate data sets from across the university has enabled development of a comprehensive platform for learning analytics. Future work will include the development of predictive models using the rapidly growing learning analytics data warehouse. Practical implications – Data warehousing infrastructure, the systematic capture and exporting of relevant library data sets are requisite for the consideration of library data in learning analytics. Originality/value – What was not anticipated five years ago when the Value Cube was first realised, was the development of learning analytic services at UOW. The Cube afforded University of Wollongong Library considerable advantage: the framework for data harvesting and analysis was established, ready for inclusion within learning analytics data sets and subsequent reporting to faculty.

scholarly journals Organizational Learning and Effectiveness: The Case of Pakistani HEIs

2019 ◽  
Vol IV (IV) ◽  
pp. 84-92
Author(s):  
Maqsood Haider ◽  
Muhammad Aamir ◽  
Khawar Naheed
Keyword(s):  
Organizational Learning ◽  
Organizational Effectiveness ◽  
Academic Staff ◽  
Higher Learning ◽  
Data Set ◽  
Staff Members ◽  
Knowledge Based ◽  
Knowledge Based Economy ◽  
Learning Institutions ◽  
Significant Direct Effect

Higher education is considered to be the lifeline of the development of any nation. These institutions are not only engaged in disseminating knowledge but are also engaged in shaping human capital for todays knowledge-based economy. These higher learning institutions need to focus on their effectiveness if they want to be learning organizations. The present study focuses on organizational learning for attaining effectiveness. The sample of the study consists of 350 academic staff members of public sector HEIs belonging to Khyber Pakhtunkhawa province of Pakistan. Multiple regression analysis was performed on the data set. The overall results showed a significant direct effect of organizational learning upon organizational effectiveness. The results are significant for the top management.

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