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Abstract:

This consistent study examined the correlation between Digital learning materials and students’ achievement in mathematics in High schools in UAE with particular reference to Sharjah School. The basic study applied a quasi-experimental research design. A sample of 50 participants out of the 200 target population was carefully chosen using the Slovene’s method. The researcher engaged sampling strategies like simple random sampling and the lottery technique to gather statistics for the research schoolwork. Facts were garnered using observation checklists, prior knowledge tests, pre-test, post-test, and motivation survey tools which were applied to the control and the treatment group. Data were scrutinized using inferential analyses, independent t-tests, paired sample t-tests, and confidence intervals of the difference with a significance level below 0.05. The investigation study findings came up with a significant correlation between Digital learning materials and students’ attainment in mathematics in Sharjah School in UAE. It was therefore concluded that the use of Digital learning materials remains very pertinent in the teaching-learning process of students in the world to help students study at their convenience and during the world pandemics. The methodical research study recommended that managers of schools should augment the budget for Digital learning materials to cater to a teaching platform that allows students to meet their teachers, make a discussion, and watch videos and presentations about the concepts from their mathematical books. These digital learning materials have to be manipulated according to students’ needs to help them understand and learn concepts in mathematics.

Keywords:

Digital learning materials,Students,Mathematics,UAE,

Refference:

I. Aguanta , A. & Tan, G. B. (2018). The Type of Vocabulary Learning Strategies Used by ESL. University Putra Malaysia. English Language Teaching, pp84-90.
II. Gunawardhana, H. (2020). Trigonometry Learning. New Horizons in Education, 57(1), 67-80.
III. Jackson, G. R. (2003).Positive interdependence: Key to effective reliability tests, pp33-45.
IV. Laufer, B., & Hill, M. (2018). What lexical information do L2 learners select in a CALL dictionary and how does it affect word retention? Language Learning & Technology, 3(2), pp 58-76.
V. Mensah, F. S. (2017). Ghanaian Senior High School students’ error in learning of trigonometry. International Journal of Environmental and Science Education, 12(0), 8.
VI. Orhun, N. (2010). The gap between real numbers and trigonometric relations. Quaderni di Ricerca in Didattica, 20, 175–184.
VII. Pierce, F., (2017). What are the Branches of Linguistics? https://www.lifepersona.com/what-are-the-branches-of-linguistics.
VIII. Ryan, J. (2019). Integrating computers into the teaching of calculus: differentiating student, pp22-81
IX. Singh, F. & Mishra, D. (2022). Language and the lexicon. An Introduction. New York: Routledge,28-79.
X. Varaidzai, C. & Makondo, K. ( 2020).Mathematics and Human life. Irwin Publishing compny, pp78-90.
XI. Vukovic, R.K., & Lesaux, N.K. (2013). Investigating the ways language counts for children’s mathematical development. Journal of Experimental Child Psychology, pp115, 227- 244. Issue 10,Volume 13.
XII. Zheng, H. & Wang, W. (2016). The Use of Electronic Dictionaries in EFL Classroom. English Language Center, Shantou University, Shantou, China, pp19-68.

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Abstract:

Ilich method has been used to evaluate the activation energy of Differential Thermal Analysis (DTA) peaks. The method uses 10-20% of the initial rise portion of the peak. The suitability of the method has been adjudged by applying it both to the synthetic and experimental DTA peaks. It is found that the method can be used irrespective of the values of kinetic parameters of the peaks

Keywords:

Ilich method,Differential Thermal Analysis,kinetic parameters,

Refference:

I. B. M. Ilich, Sov.Phy-Sol.St. 21 1880 (1979).

II. CC Huang and T.S. Wu, Thermochim Acta 204 239(1992).

III. D.C.Sanyal and K Das, “A text book of Numerical Analysis” (U.N.Dhar, Kolkata), 2012.

IV. L.K. Singh and S. Mitra , J Chem. Soc. Dalton Trans, 21 2089, (1987).

V. M. Abramowitz and I.A. Stegun (Eds), “Hand Book of Mathematical Functions” (Dover, New York) Ch 5 (1965).

VI. M. Karmakar, Sk Azharuddin, S.Barman, PS Mazumdar and S D Singh. Material Science Research. 6.189 (2009).

VII. R.Chen and Y.Krish, “Analysis Of Thermally Stimulated Processes” (Oxford, Pergamon) (1981).

VIII. R.K. Gatria and H. N. K. Sarma, “Deconvoluation Methods in Thermally Stimulated processes” (Eureka Publishers, New Delhi), (1998).
IX. Sk Azaharuddin, B.Ghosh, A.Sarkar, S. Bhattacharya and P.S.Majumder J Mech Cont & Math Sci. Vol- 14, 121 (2019).
IX. S.K. Azaharuddin, B.Ghosh, S.Ghosh and P.S.Majumder, (J. Mech Cont & Math Sci.) 13, 29 (2018).

X. S.K. Azaharuddin, S.D.Singh and P.S.Majumdar J. Mech Cont & Math Sci. 12, 10 (2018).

XI. T.T. Yang and M. Steinberg. Anal chem 49, 998(1977).

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Abstract:

The time series analysis of chlorophyll a was carried out for more than 3 decades (1984-2018) from the coastal water of Digha and the data bank were subject to Nonlinear Autoregressive Neural Network Model to evaluate the status of the coastal water in 2050. The concentration of chlorophyll a ranged between 1.05 mgm^-3 (in 2009) to 5.16 mgm^-3 (in 1984) during the span of 35 years (real-time data). Chlorophyll a has a great role to drive the marine and estuarine food chain as it acts as the engine to transfer the energy derived from the Sun through different tires of the food chain. The decreasing trend of chlorophyll a with time is a warning signal for the fishery products from the region as the phytoplankton containing chlorophyll a serve as the major food of the fishes.

Keywords:

Chlorophyll a,Nonlinear Autoregressive ,Neural Network Model,Digha coast,,food chain,phytoplankton.,

Refference:

I. Jeffrey, S. W., Humphrey, G. F., “New spectrophotometric equations for determining chlorophyll a, b, c1 and c2 in higher plants, algae and natural phytoplankton”, Biochemie und Physiologie der Pflanzen, Vol. 167, pp: 191-19, 1975.
II. Mitra, A., “Ecosystem services of mangroves: An overview”, published by Springer. ISBN 978-81-322- 2106-7, DOI: 10.1007/978-3-030-20595-9_1, 2020.
III. Mitra, A., “Sensitivity of Mangrove ecosystem to changing Climate”, published by Springer. DOI: 10.1007/978-; 81-322-1509-7. Pp: 323, 2013.
IV. Mitra, A., Zaman, S., “Basics of Marine and Estuarine Ecology”, published by Springer. ISBN 978-81- 322-2705-2, 2016.
V. Mitra, A., Zaman, S., “Blue carbon reservoir of the blue planet”, published by Springer. ISBN 978-81-322-2106-7 (Springer DOI 10.1007/978- 81-322-2107-4), 2015.
VI. Mitra, A., Zaman, S., “Estuarine acidification”, published by Springer, ISBN 978-3-030-84792-0, 2021.
VII. https://earthobservatory.nasa.gov/global-maps

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