Innovare Journal of Social Sciences

MODIFYING A WHATSAPP GROUP TO ENHANCE SPEECH SKILLS THROUGH PODCASTS AND COMMUNICATIVE GRAMMAR

2024-09-10T07:11:45+0530

Fifteen EFL students experienced problems in pouring their ideas in spoken and written language to fix them, so they joined the treatment class. To get started the teacher created a WhatsApp group application to conduct the teaching–learning process. The materials used in the lesson are lexico-grammar, communicative grammar, noticing, and retelling. The resource of the lesson is taken from podcasts, YouTube, and pictures. All the materials are presented in each unit. It started with lexico-grammar using the grammar-translation method. To emphasize the implementation of the discussed grammar in the text, the teacher invited the students to notice the text from the podcast. Moreover, the teacher asked them to listen to the recording and translate the text to strengthen their vocabulary building. To meet the goal of learning a language, the students described the pictures based on the discussed grammar. Using WhatsApp application eases the students to get the assessment, and feedback from the teacher. The information gathered during the teaching and learning process was recorded to conduct a qualitative analysis. The analysis stated that implementing communicative grammar learning through a WhatsApp group application helps students to develop their ability to deliver ideas in spoken and written language.

INVESTIGATING THE IMPACT OF LITHIUM MINING ON SOIL QUALITY, AND PLANT GROWTH IN ANGWA-KEDE COMMUNITY, KOKONA LGA, NASARAWA STATE, NIGERIA

2024-09-08T04:21:10+0530

Transition to green energy has made lithium mining one of the biggest venture in the world but this comes with its implications. This research was conducted to check the level of lithium mining contamination on soil quality and on plant in Angwa-Kede community due to observed poor agricultural yield. A systematic sampling method was conducted on both plant and soil samples from mining site and host community of Angwa-Kede to check the effects of lithium mining activity. The obtained plant and soil samples were analyzed using X-ray flourescence analysis (XRF) analysis to check elemental composition and the nutrient dynamics of both plant and soil. The XRF result revealed that soil samples from host community displayed higher level of Aluminum (Al) concentration in soil ranging from 20.48 to 31.18% Al indicating high contamination. Flame test results of plant samples from lithium mining site contains 0.466–0.477 ppm Li while those from host community has lithium concentration ranging from 0.0139 to 0.194 ppm Li which is above the accumulated level of lithium concentration in the blood (0.01374–0.02748 ppm Li) an indication of toxicity to human health. Soil samples of the mining site having risk factor (Rf) of 139.76, 168.49, and 350.26 while the soil samples from the host community has Rf of 7,194.24, 10,810.81, and 14,388.48, respectively. In conclusion, the obtained result shows high level of soil and plant lithium contamination in Angwa-Kede community which is caused by uncontrolled lithium mining method and poor waste disposal system.

INVESTIGATING WATER CONTAMINATION BY LITHIUM MINING ACTIVITY IN ANGWA-KEDE, KOKONA LGA, NASARAWA STATE, NIGERIA

2024-09-08T04:31:06+0530

Lithium mining in Nigeria poses lots of concern due to the uncontrolled mining practices used at different mining site as seen in various states where lithium ore is mined. Due to this, water samples were obtained from the host community and lithium mining pits in Angwa-Kede, Kokona LGA, Nasarawa State, Nigeria to check the level of lithium contamination in the water samples. The water samples were taken to the laboratory and analyzed using flame test analysis. Water sample results from the host community revealed that the lithium concentration ranges from 0.0093 to 0.0325 ppm Li which is higher than the standard value of 0.01 ppm Li thus indicating water toxicity while water samples from mining site ranges from 0.152 to 0.788 ppm Li. The mining risk factor (Rf) values for water quality at the mining site were found to be 0.012, 0.02, 0.034, 0.065, 0.154 which are quite low and this can be attributed to constant dewatering process at the mining site while the mining Rf values for water samples from host community were found to be 1.098 (River: Moderately high) and 0.285 (Borehole: Low), respectively. In conclusion, the flame test analysis result for all the water samples from the host community (River and Bore Hole) reveals the presence of lithium at concentrations that appear to be detrimental to human health. Accumulative amount of such lithium concentration in human body/blood could result to bipolar disease hence the need for water treatment and a more controlled mining practice.

UNLOCKING THE TRANSFORMATIVE POWER OF ARTIFICIAL INTELLIGENCE FOR SUSTAINABLE DEVELOPMENT IN HIGHER EDUCATION

2024-09-21T15:06:14+0530

The rapid transformative power advancement of Artificial Intelligence (AI) is revolutionising various aspects of higher education, offering a transformative potential to reshape the way higher education teachers and students learn, teach, and interact as the global higher education sector strives to achieve sustainable development. Artificial intelligence has become a current phenomenon that everyone needs to tap into to promote inclusive and equitable access and drive innovation in teaching and learning environments through staff and students' perceptions. The study approach employed Structural Equation Modeling and gathered staff and students' perceptions of the potential AI sustainability in Higher Education. Two hundred fifty (250) samples were gathered using cluster and multi-stage sampling methods based on the study population. The researcher disseminated surveys through face-to-face and social media platforms, including WhatsApp. The data was analysed using two different software, AMOS and SPSS, and the outcome of the data collected based on the relationship of variables towards adaptation of AI in higher institutions of learning for a better educational system and enhancing qualities of education based on a set of descriptive and testing the relationship between four different variables. The findings revealed that artificial intelligence adoption in higher education enhances and transform the educational system. The study identified that awareness, attitude, and performance expectancy play significant roles in influencing AI adaptation in Higher education. The study recommends that policymakers, educators, and institutions harness the transformative potential of AI for sustainable higher educational development, emphasising the importance of collaboration, professional development, and ethical standards in enhancing higher education to become more effective, efficient, and inclusive, and ultimately contributing to a more sustainable future for individuals and society.