Integrating Emerging Technologies into Guidance and Counselling for Sustainable Development in Public Universities in South-East, Nigeria
Department of Guidance and Counselling, Enugu State University of Science and Technology, Agbani, Enugu State, Nigeria
asogwa.solomon@esut.edu.ng
Abstract
This study determined the extent to which emerging technologies are integrated into guidance and counselling for sustainable development in public universities in South-East, Nigeria. Using a descriptive survey research design, all 107 guidance counselling lecturers (63 female and 44 male) in seven public universities in South-East, Nigeria were studied. A 21-item questionnaire with overall reliability coefficient of 0.57 was used for data collection. Mean, standard deviation, and t-test at 0.05 significance level were used for analysis. Results showed that Machine Learning (ML) was integrated into guidance and counselling to a low extent (cluster mean = 1.76), and Live Streaming was also integrated to a low extent (cluster mean = 1.85). No significant difference was found between male and female guidance counsellors' mean scores on either variable. The study recommends that government and university administrators should adopt Machine Learning and Live Streaming for sustainable development of public universities in South-East, Nigeria.
Keywords
Introduction
Technology has affected every spectrum of our lives and businesses, leading to huge effects in the way we carry out daily routines. Guidance and counselling is a confidential dialogue between a client and a counsellor aimed at enabling the client to cope with stress and take personal decisions. It assists learners in harmonizing their values, interests, and abilities towards developing their full potentials in school. In Nigeria, guidance and counselling has been well received by government and private sectors as a programme meant to help students adjust meaningfully to the environment and develop the ability to set realistic goals. However, guidance and counselling programs within South-East Nigeria are challenged by poor integration of emerging technology needed for sustainable development in public universities. Emerging technologies are those technologies likely to have a large impact on teaching, learning, or creative inquiry, bringing paradigm changes at a very rapid pace with respect to the digital world. Utilization of emerging technology tools for counselling in universities could yield productive results necessary for sustainable development and achievement of university goals.
Machine Learning in Guidance and Counselling
Machine Learning (ML) is a core branch of Artificial Intelligence that focuses on developing algorithms that allow machines to learn from data instead of being explicitly programmed for every task. ML systems identify patterns and make decisions based on data. ML is used by counsellors to identify students who need extra support, analyze their learning effectiveness, and make data-driven decisions about classroom practices. ML can analyze large datasets of student information to identify patterns, trends, and potential areas for improvement in teaching and learning. It is used to analyze students' performance and predict future academic outcomes, identifying students who may be at risk of falling behind. ML can also analyze student data on academic performance, skills, and interests to recommend suitable career paths and fields of study, and can automatically grade certain types of assessments. Despite the relevance of ML in universities, no empirical evidence has been shown of ML integration with regards to guidance and counselling in universities in South East, Nigeria.
Live Streaming in Guidance and Counselling
Live streaming refers to the delivery of video or audio data to an audience over the internet as the data is created. It is an interactive form of social media applications that enables viewers to achieve a variety of goals, such as acquiring useful information and gaining social support. The adoption of live streaming for online education via mobile devices provides an opportunity to address time constraints and limited resources. Online users of live streaming have reached 433 million in China, accounting for more than half of the total internet population. Increased awareness of guidance and counselling can be achieved through live streaming. The combination of live teaching through live streaming platforms and social communication through chat software can greatly improve the intimacy between counsellors and students. However, there has been little research on the use of live streaming platforms for formal education particularly in Nigeria, creating a gap that this study sought to fill.
Methodology
Descriptive survey research design was utilized for this study. The population comprised all 107 guidance and counselling lecturers (63 female and 44 male) in seven public universities in South-East, Nigeria. No sampling was involved due to the manageable population size. A 21-item questionnaire titled "Integrating Emerging Technologies into Guidance and Counselling for Sustainable Development Questionnaire (IETGCSDQ)" was used for data collection, structured on a 4-point scale of Very Great Extent, Great Extent, Little Extent, and Very Little Extent. The instrument was validated by three research experts and tested for reliability using Cronbach Alpha, yielding an overall reliability coefficient of 0.57. Out of 107 administered instruments, 103 copies were correctly filled and returned. Mean and standard deviation were used to answer research questions, while t-test statistic was used to test null hypotheses at 0.05 significance level.
Results
Research question one revealed that Machine Learning was integrated into guidance and counselling for sustainable development in public universities in South-East, Nigeria to a low extent, with an overall cluster mean of 1.76. Items 10, 11, and 12 indicated very low extent (means of 1.49, 1.34, and 1.44 respectively), while remaining items ranged from 1.34 to 2.18. The t-test analysis showed no significant difference between male and female guidance counsellors' mean scores on ML integration (t = 0.101, df = 101, p = .920). Research question two revealed that Live Streaming was also integrated to a low extent, with an overall cluster mean of 1.85, with all items showing low extent (means ranging from 1.63 to 2.08). The t-test analysis showed no significant difference between male and female guidance counsellors' mean scores on Live Streaming integration (t = .013, df = 101, p = .990). Both null hypotheses were therefore not rejected.
Discussion and Conclusion
The findings that both Machine Learning and Live Streaming were integrated into guidance and counselling to a low extent contradict previous research showing the benefits of these technologies in educational settings. The findings indicate that government and university administrators need to do more to enhance emerging technology tools usage in universities in South East, Nigeria. There is an indication that university authorities of public universities in South East do not have any policy document regarding integration of emerging technologies. The study concluded that there was no significant difference between male and female guidance counsellors on the extent to which Machine Learning and Live Streaming were integrated into guidance and counselling for sustainable development. Government and university administrators should adopt Machine Learning for sustainable development of public universities in South-East, Nigeria. Efforts should be made by university management to utilize Live Streaming for sustainable development. Deliberate efforts should be made by government and university management to assist in integrating these emerging technologies into guidance and counselling for sustainable development in public universities in South-East, Nigeria.