Online Project Based Collaborative Learning Model To Enhance Students Soft Skills

Collaborative Learning (CL) has been proven to promote soft skills and has widely implemented in teaching and learning. However, initial study found the lack of soft skills issue among Malaysian Polytechnic graduates causing the students to face unemployment. Therefore, the purpose of this study is...

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Main Author: Razali, Sharifah Nadiyah
Format: Thesis
Language:English
English
Published: 2016
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Online Access:http://eprints.utem.edu.my/id/eprint/18565/1/Online%20Project%20Based%20Collaborative%20Learning%20Model%20To%20Enhance%20Students%20Soft%20Skills%2024%20Pages.pdf
http://eprints.utem.edu.my/id/eprint/18565/2/Online%20Project%20Based%20Collaborative%20Learning%20Model%20To%20Enhance%20Students%20Soft%20Skills.pdf
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institution Universiti Teknikal Malaysia Melaka
collection UTeM Repository
language English
English
advisor Shahbodin, Faaizah

topic L Education (General)
LB Theory and practice of education
spellingShingle L Education (General)
LB Theory and practice of education
Razali, Sharifah Nadiyah
Online Project Based Collaborative Learning Model To Enhance Students Soft Skills
description Collaborative Learning (CL) has been proven to promote soft skills and has widely implemented in teaching and learning. However, initial study found the lack of soft skills issue among Malaysian Polytechnic graduates causing the students to face unemployment. Therefore, the purpose of this study is to evaluate the effectiveness of Online Project Based Collaborative Learning (OPBCL) model in enhancing students’ soft skills. This study involves both qualitative and quantitative methods. This study has been divided into three phases which are (i) Analysis; (ii) Design and Development; and (iii) Implementation and Evaluation. In the analysis phase, all factors and elements that affect the effectiveness of Online Collaborative Learning (OCL) were identified through document review and validated by experts. At the end of this phases, an OCL model was proposed. In the design and development phase, OPBCL was designed and developed based on the proposed model. Besides that, three testing instruments were developed to assess the effectiveness of OPBCL which are (i) Pre and Post Soft Skill Test (SST), (ii) Collaborative Learning Rubric (CLR) and (iii) Perception of Online Collaborative Learning Questionnaire (POCLQ). Pilot study was conducted in order to ensure that all the instruments are valid and reliable. Finally, pre and post-test with non-equivalent control group design were used in implementation and evaluation phase which involves 106 respondents from Malaysian Polytechnic. The respondents were divided into three groups called Control, Treatment I and Treatment II group where their soft skills are assessed for comparison between traditional project based learning method (Control), online project based learning using CIDOS platform (Treatment I) and online project based learning using OPBCL platform (Treatment II). All collected data were analysed using SPSS 19.0 software. Findings from the pre and post soft skill indicated that all groups had positive effects on the soft skills of the students but in terms of the more successful group, the results showed that Treatment II is more success than Control group followed by Treatment I group. In addition, findings on the pre and post soft skills test of the critical thinking and problem solving (CTPS), collaboration (CS) and communication (CM) skills showed that for CTSP skill, Treatment II is more success than Control group followed by Treatment I group. For CS skill, there is no significant differences between Treatment I and Treatment II group. However, both treatment groups are more success than Control group. For CM skill, there is no significant differences between Control and Treatment II group. However, both Control and Treatment II groups are more success than Treatment I group. In conclusion, the proposed OPBCL model has shown an enhancement in students’ soft skills.
format Thesis
qualification_name other
qualification_level Doctorate
author Razali, Sharifah Nadiyah
author_facet Razali, Sharifah Nadiyah
author_sort Razali, Sharifah Nadiyah
title Online Project Based Collaborative Learning Model To Enhance Students Soft Skills
title_short Online Project Based Collaborative Learning Model To Enhance Students Soft Skills
title_full Online Project Based Collaborative Learning Model To Enhance Students Soft Skills
title_fullStr Online Project Based Collaborative Learning Model To Enhance Students Soft Skills
title_full_unstemmed Online Project Based Collaborative Learning Model To Enhance Students Soft Skills
title_sort online project based collaborative learning model to enhance students soft skills
granting_institution Universiti Teknikal Malaysia Melaka
granting_department Faculty of Information and Communication Technology
publishDate 2016
url http://eprints.utem.edu.my/id/eprint/18565/1/Online%20Project%20Based%20Collaborative%20Learning%20Model%20To%20Enhance%20Students%20Soft%20Skills%2024%20Pages.pdf
http://eprints.utem.edu.my/id/eprint/18565/2/Online%20Project%20Based%20Collaborative%20Learning%20Model%20To%20Enhance%20Students%20Soft%20Skills.pdf
_version_ 1747833936348708864
spelling my-utem-ep.185652021-10-08T15:26:06Z Online Project Based Collaborative Learning Model To Enhance Students Soft Skills 2016 Razali, Sharifah Nadiyah L Education (General) LB Theory and practice of education Collaborative Learning (CL) has been proven to promote soft skills and has widely implemented in teaching and learning. However, initial study found the lack of soft skills issue among Malaysian Polytechnic graduates causing the students to face unemployment. Therefore, the purpose of this study is to evaluate the effectiveness of Online Project Based Collaborative Learning (OPBCL) model in enhancing students’ soft skills. This study involves both qualitative and quantitative methods. This study has been divided into three phases which are (i) Analysis; (ii) Design and Development; and (iii) Implementation and Evaluation. In the analysis phase, all factors and elements that affect the effectiveness of Online Collaborative Learning (OCL) were identified through document review and validated by experts. At the end of this phases, an OCL model was proposed. In the design and development phase, OPBCL was designed and developed based on the proposed model. Besides that, three testing instruments were developed to assess the effectiveness of OPBCL which are (i) Pre and Post Soft Skill Test (SST), (ii) Collaborative Learning Rubric (CLR) and (iii) Perception of Online Collaborative Learning Questionnaire (POCLQ). Pilot study was conducted in order to ensure that all the instruments are valid and reliable. Finally, pre and post-test with non-equivalent control group design were used in implementation and evaluation phase which involves 106 respondents from Malaysian Polytechnic. The respondents were divided into three groups called Control, Treatment I and Treatment II group where their soft skills are assessed for comparison between traditional project based learning method (Control), online project based learning using CIDOS platform (Treatment I) and online project based learning using OPBCL platform (Treatment II). All collected data were analysed using SPSS 19.0 software. Findings from the pre and post soft skill indicated that all groups had positive effects on the soft skills of the students but in terms of the more successful group, the results showed that Treatment II is more success than Control group followed by Treatment I group. In addition, findings on the pre and post soft skills test of the critical thinking and problem solving (CTPS), collaboration (CS) and communication (CM) skills showed that for CTSP skill, Treatment II is more success than Control group followed by Treatment I group. For CS skill, there is no significant differences between Treatment I and Treatment II group. However, both treatment groups are more success than Control group. For CM skill, there is no significant differences between Control and Treatment II group. However, both Control and Treatment II groups are more success than Treatment I group. In conclusion, the proposed OPBCL model has shown an enhancement in students’ soft skills. UTeM 2016 Thesis http://eprints.utem.edu.my/id/eprint/18565/ http://eprints.utem.edu.my/id/eprint/18565/1/Online%20Project%20Based%20Collaborative%20Learning%20Model%20To%20Enhance%20Students%20Soft%20Skills%2024%20Pages.pdf text en public http://eprints.utem.edu.my/id/eprint/18565/2/Online%20Project%20Based%20Collaborative%20Learning%20Model%20To%20Enhance%20Students%20Soft%20Skills.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=100364 other doctoral Universiti Teknikal Malaysia Melaka Faculty of Information and Communication Technology Shahbodin, Faaizah 1. AccuWeather, (2016). Malaysia Weather - AccuWeather.com. Available at: http://www.accuweather.com/en/my/malaysia-weather [Accessed 14 Aug. 2015]. 2. A Short History of Photovoltaic (PV) Cells. (2009). 1st ed. Genesis Energy. Available at: http://www.schoolgen.co.nz/pdf/D2%20solar%20factsheet.pdf [Accessed 20 Oct. 2015]. 3. Al-Gahtani, S., 2015. Analytical Study of the Effects of Grid Resistance on Grid-Connected PV Systems: Modeling and Simulation. Doctoral dissertation, Auburn University. 4. Anishetty, L. (2011). Schottky behavior of organic solar cells with different cathode deposition methods. Master of Science Degree in Electrical Engineering. The University of Toledo. 5. Azhari, A., Sopian, K., Zaharim, A. and Al-Ghoul, M. (2008). A new approach for predicting solar radiation in tropical environment using satellite images - Case study of Malaysia. WSEAS Trans. Environ. Dev., 4, pp.373-378. 6. Azhari, A., Sopian, K., Zaharim, A. and Al Ghoul, M. (2008). Solar Radiation Maps from Satellite Data for a Tropical Environment – Case Study of Malaysia. 3rd IASME/WSEAS Int. Conf. on Energy & Environment, University of Cambridge, UK, pp.528-533. 7. Brenna, M., Dolara, A., Foiadelli, F., Lazaroiu, G. and Leva, S. (2012). Transient Analysis of Large Scale PV Systems with Floating DC Section. Energies, 5(12), pp.3736-3752. 8. Burger, B. and Rüther, R. (2006). Inverter sizing of grid-connected photovoltaic systems in the light of local solar resource distribution characteristics and temperature. Solar Energy, 80(1), pp.32-45. 9. Chen, S., Li, P., Brady, D. and Lehman, B. (2013). Determining the optimum grid-connected photovoltaic inverter size. Solar Energy, 87, pp.96-116. 10. Chouder, A. and Silvestre, S. (2009). Analysis Model of Mismatch Power Losses in PV Systems. Journal of Solar Energy Engineering, 131(2), p.024504. 11. Clark, W. (2010). Sustainable communities design handbook. Burlington, MA: Butterworth-Heinemann. 12. Coventry, J. and Lovegrove, K. (2003). Development of an approach to compare the ‘value’ of electrical and thermal output from a domestic PV/thermal system. Solar Energy, 75(1), pp.63-72. 13. Cullen, R. (2009). What is Maximum Power Point Tracking (MPPT) and How Does it Work? Blue Sky Energy, Inc. Available at: http://www.blueskyenergyinc.com/uploads/pdf/BSE_What_is_MPPT.pdf. [Accessed 16 Nov. 2015]. 14. De Brito, M.A., Sampaio, L.P., Junior, L.G. and Canesin, C., 2011, September. Research on photovoltaics: review, trends and perspectives. In Power Electronics Conference (COBEP), 2011 Brazilian (pp. 531-537). 15. Demoulias, C. (2010). A new simple analytical method for calculating the optimum inverter size in grid-connected PV plants. Electric Power Systems Research, 80(10), pp.1197-1204. 16. Denholm, P., Margolis, R., Mai, T., Brinkman, G., Drury, E., Hand, M. and Mowers, M. (2013). Bright Future: Solar Power as a Major Contributor to the U.S. Grid. IEEE Power and Energy Magazine, 11(2), pp.22-32. 17. Engel-Cox, J., Nair, N. and Ford, J. (2012). Evaluation of Solar and Meteorological Data Relevant to Solar Energy Technology Performance in Malaysia. Journal of Sustainable Energy & Environment, (3), pp.115-124. 18. Epu.gov.my, (2015). Official Website of Economic Planning Unit - Tenth Malaysia Plan (10th MP). Available at: http://www.epu.gov.my/en/tenth-malaysia-plan-10th-mp [Accessed 11 Oct. 2015]. 19. Ertan, H.B., Doğru, E. and Yılmaz, A., 2012, May. Comparison of efficiency of two dc-to-ac converters for grid connected solar applications. In Optimization of Electrical and Electronic Equipment (OPTIM), 2012 13th International Conference on (pp. 879-886). 20. Ettah, E.B., Obiefuna, J.N. and Njar, G.N., 2011. The relationship between solar radiation and the efficiency of solar panels in Port Harcourt, Nigeria. International Journal of Applied, 1(4), pp. 124-126. 21. Fayaz, H., Rahim, N., Saidur, R., Solangi, K., Niaz, H. and Hossain, M. (2011). Solar energy policy: Malaysia vs developed countries. 2011 IEEE Conference on Clean Energy and Technology (CET), 5, pp.374 - 378. 22. Florida Solar Energy Centre, (2015). Types of PV Systems. Available at: http://www.fsec.ucf.edu/en/consumer/solar_electricity/basics/types_of_pv.htm [Accessed 12 Nov. 2015]. 23. Gan, C.K., Lee, Y.M., Pudjianto, D. and Strbac, G., 2014, September. Role of losses in design of DC cable for solar PV applications. In Power Engineering Conference (AUPEC), 2014 Australasian Universities (pp. 1-5). 24. Gevorkian, P. (2008). Solar power in building design. New York: McGraw-Hill. 25. Ghoddami, H. (2013). Enhanced Voltage-Sourced Inverters for Large-Scale Grid-Connected Photovoltaic Systems. Electronic Thesis and Dissertation Repository. 26. Global Sustainable Energy Solutions, (2012). Grid Connected PV systems Design and Installation. 7th ed. global sustainable energy solutions (GSES), p.236. 27. Handbook on the Malaysian Feed-in Tariff for the Promotion of Renewable Energy, (2011). Ministry of Energy, Green Technology and Water Malaysia. 28. Hashim, H. and Ho, W. (2011). Renewable energy policies and initiatives for a sustainable energy future in Malaysia. Renewable and Sustainable Energy Reviews, 15(9), pp.4780-4787. 29. Hdr.undp.org, (2015). Human Development Reports | United Nations Development Programme. Available at: http://hdr.undp.org/en [Accessed 8 Oct. 2015]. 30. Hernández, J., De la Cruz, J. and Ogayar, B. (2012). Electrical protection for the grid-interconnection of photovoltaic-distributed generation. Electric Power Systems Research, 89, pp.85-99. 31. Hossain, M.A., 2014. Thermal characteristics of micro inverters on dual-axis trackers. (Doctoral dissertation, Case Western Reserve University). 32. Islam, M., Rahman, M.Z. and Mominuzzaman, S.M., 2014, May. The effect of irradiation on different parameters of monocrystalline photovoltaic solar cell. In Developments in Renewable Energy Technology (ICDRET), 2014 3rd International Conference (pp. 1-6) 33. Jansson, P.M., Schwabe, U.K.W. and Hak, A., Large-Scale Photovoltaic System Design: Learning Sustainability through Engineering Clinics. In 2008 ASEE Annual Conference Proceedings, Pittsburgh, PA (June 22-25, 2008). 34. Kaizuka, I., Ohigashi, T., Matsukawa, H. and Ikki, O. (2009). PV trends in Japan: New framework for introduction of PV system. Photovoltaic Specialists Conference (PVSC), 2009 34th IEEE, 5, pp.712-716. 35. Kamaruzzaman, S., Abdul-Rahman, H., Wang, C., Karim, S. and Lee, T. (2012). Solar technology and building implementation in Malaysia: A national paradigm shift. Maejo International Journal of Science and Technology, 6(2), pp.196-215. 36. Kaushika, N. and Rai, A. (2006). Solar PV design aid expert system. Solar Energy Materials and Solar Cells, 90(17), pp.2829-2845. 37. Kerekes, T., Koutroulis, E., Séra, D., Teodorescu, R. and Katsanevakis, M. (2013). An Optimization Method for Designing Large PV Plants. IEEE Journal of Photovoltaics, 3(2), pp.814-822. 38. Khatib, T., Mohamed, A. and Sopian, K. (2013). A review of photovoltaic systems size optimization techniques. Renewable and Sustainable Energy Reviews, 22, pp.454-465. 39. Kishor, N., Villalva, M., Mohanty, S. and Ruppert, E. (2010). Modeling of PV Module with Consideration of Environmental Factors. In ISGT Europe, pp.1-5. 40. Kjær, S. (2005). Design and Control of an Inverter for Photovoltaic Applications. Doctor of Philosophy. Aalborg Universitet: Institut for Energiteknik, Aalborg Universitet. 41. Lian, K., Jhang, J. and Tian, I. (2014). A Maximum Power Point Tracking Method Based on Perturb-and-Observe Combined With Particle Swarm Optimization. IEEE Journal of Photovoltaics, 4(2), pp.626-633. 42. Liang, Z. (2011). High Efficiency Distributed Solar Energy Conversion Techniques. Doctor of Philosophy. North Carolina State University. 43. Luoma, J., Kleissl, J. and Murray, K. (2012). Optimal inverter sizing considering cloud enhancement. Solar Energy, 86(1), pp.421-429. 44. Maillo, L.R. (2013). Application Note Optimal Cable Sizing in PV Systems: Case Study. European Copper Institute, Leonardo Energy, (2). 45. Malamaki, K. and Demoulias, C. (2014). Analytical Calculation of the Electrical Energy Losses on Fixed-Mounted PV Plants. IEEE Trans. Sustain. Energy, 5(4), pp.1080-1089. 46. Malamaki, K. and Demoulias, C. (2013). Minimization of Electrical Losses in Two-Axis Tracking PV Systems. IEEE Transactions on Power Delivery, 28(4), pp.2445-2455. 47. Malek, H. (2014). Control of Grid-Connected Photovoltaic Systems Using Fractional Order Operators. Doctor of Philosophy. Utah State University. 48. Malwe, H., Djongyang, N., Doka, S., Moutala, Y., Moutala, Y., Gambo, B., Moutala, Y. and Kofané, T. (2014). Reducing electrical energy losses in photovoltaic source distribution networks. International Journal of Basic and Applied Sciences, 3(3). 49. Market Report 2011. (2012). European Photovoltaic Industry Association. Available at: http://www.sapvia.co.za/european-photovoltaic-industry-association-market-report-2011/ [Accessed 23 Oct. 2015]. 50. Martins, D. (2013). Analysis of a Three-Phase Grid-Connected PV Power System Using a Modified Dual-Stage Inverter. ISRN Renewable Energy, 2013, pp.1-18. 51. Massi Pavan, A., Mellit, A. and De Pieri, D. (2011). The effect of soiling on energy production for large-scale photovoltaic plants. Solar Energy, 85(5), pp.1128-1136. 52. Mbipv.net.my, (2015). Mbipv Project. Available at: http://www.mbipv.net.my/content.asp?higherID=5&zoneid=4&categoryid=6&hghid=5 [Accessed 4 Nov. 2015]. 53. Mbipv.net.my, (2015). Mbipv Project. Available at: http://www.mbipv.net.my/content.asp?zoneid=1&categoryid=10 [Accessed 4 Nov. 2015]. 54. Mehleri, E., Zervas, P., Sarimveis, H., Palyvos, J. and Markatos, N. (2010). Determination of the optimal tilt angle and orientation for solar photovoltaic arrays. Renewable Energy, 35(11), pp.2468-2475. 55. Mellit, A. and Pavan, A. (2010). Performance prediction of 20kWp grid-connected photovoltaic plant at Trieste (Italy) using artificial neural network. Energy Conversion and Management, 51(12), pp.2431-2441. 56. Mohamed Nawawi, N., Gan, C.K. and Tan, P.H., 2015, August. Comparison of Different Options to Transport Energy in Photovoltaic Power Plant Using Centralized Inverter. In Applied Mechanics and Materials (Vol. 785, pp. 596-600). Trans Tech Publications. 57. Mpoweruk.com, (2015). Electricity Generation from Solar Energy, Technology and Economics. Available at: http://mpoweruk.com/solar_power.htm [Accessed 10 Nov. 2015]. 58. Muhammad-Sukki, F., Abu-Bakar, S., Munir, A., Mohd Yasin, S., Ramirez-Iniguez, R., McMeekin, S., Stewart, B. and Abdul Rahim, R. (2014). Progress of feed-in tariff in Malaysia: A year after. Energy Policy, 67, pp.618-625. 59. Muzathik, A., Nik, W., Samo, K. and Ibrahim, M. (2010). Hourly Global Solar Radiation Estimates on a Horizontal Plane. Journal of Physical Science, 21(2), pp.51-66. 60. MS 1837: 2010 Malaysia Standard, 2010. Installation of Grid Connected Photovoltaic (PV) System (First revision), Department of Standards Malaysia. 61. Notton, G., Lazarov, V. and Stoyanov, L. (2010). Optimal sizing of a grid-connected PV system for various PV module technologies and inclinations, inverter efficiency characteristics and locations. Renewable Energy, 35(2), pp.541-554. 62. Ogimoto, K., Kaizuka, I., Ueda, Y. and Oozeki, T. (2013). A Good Fit: Japan's Solar Power Program and Prospects for the New Power System. IEEE Power and Energy Magazine, 11(2), pp.65-74. 63. Oh, T., Pang, S. and Chua, S. (2010). Energy policy and alternative energy in Malaysia: Issues and challenges for sustainable growth. Renewable and Sustainable Energy Reviews, 14(4), pp.1241-1252. 64. Ong, T. and Thum, C. (2013). Net Present Value and Payback Period for Building Integrated Photovoltaic Projects in Malaysia. International Journal of Academic Research in Business and Social Sciences, 3(2), pp.153-171. 65. Pasolar.ncat.org, (2015). Lesson 5: Photovoltaics (PV) System Basics | Pennsylvania Solar Course. Available at: http://www.pasolar.ncat.org/lesson05.php#types [Accessed 13 Nov. 2015]. 66. Patel, H. and Agarwal, V. (2008). MATLAB-Based Modeling to Study the Effects of Partial Shading on PV Array Characteristics. IEEE Transactions on Energy Conversion, 23(1), pp.302-310. 67. Pearsall, N.M., 2011. PV research and development in Europe — A view from the Technology Platform. 2011 37th IEEE Photovoltaic Specialists Conference, pp.200–205. 68. Petrone, G., Spagnuolo, G. and Vitelli, M. (2007). Analytical model of mismatched photovoltaic fields by means of Lambert W-function. Solar Energy Materials and Solar Cells, 91(18), pp.1652-1657. 69. Pvpowerway.com, (2015). PV Modules and Other Components_Basics Knowledge_Powerway, Your Professional solar Farm Builder. Available at: http://www.pvpowerway.com/en/knowledge/modules.html [Accessed 9 Dec. 2015]. 70. Renewable Energy Technologies: Cost Analysis Series. (2012). International Renewable Energy Agency (IRENA). Available at: https://www.irena.org/DocumentDownloads/Publications/RE_Technologies_Cost_Analysis- Solar_PV.pdf [Accessed 23 Oct. 2015]. 71. Richter, A., Hermle, M. and Glunz, S. (2013). Reassessment of the Limiting Efficiency for Crystalline Silicon Solar Cells. IEEE Journal of Photovoltaics, 3(4), pp.1184-1191. 72. Ross, M. (2005). Optimal Wire Size for Photovoltaic Systems Operating at Maximum Power Point: A Closed Form Approach. Proceedings of the 30th Annual Conference of the Solar Energy Society of Canada, Burnaby, BC, 20. 73. Roy, B., Basu, A. and Paul, S. (2013). Optimal Design of a Grid Connected Solar Photovoltaic Power System for a Residential Load. International Journal of Recent Advances in Engineering & Technology (IJRAET), 1(3), pp.95-100. 74. Salmi, T., Bouzguenda, M., Gastli, A. and Masmoudi, A. (2012). MATLAB/Simulink Based Modelling of Solar Photovoltaic Cell. International Journal of Renewable Energy Research, 2(2), pp.213-218. 75. Schönberger, J., 2009, September. A single phase multi-string PV inverter with minimal bus capacitance. In Power Electronics and Applications, 2009. EPE'09. 13th European Conference on (pp. 1-10). 76. Seda.gov.my, (2015). SEDA PORTAL. Available at: http://seda.gov.my [Accessed 11 Oct. 2015]. 77. Shams El-Dein, M., Kazerani, M. and Salama, M. (2013). Optimal Photovoltaic Array Reconfiguration to Reduce Partial Shading Losses. IEEE Trans. Sustain. Energy, 4(1), pp.145-153. 78. Shamsiah, A. (2013). Cable care. PV magazine. Available at: http://www.pv-magazine.com/archive/articles/beitrag/cable-care-_100011160/572/#axzz43QbIh9H0 [Accessed 19 Oct. 2015]. 79. Shamsuddin, A. (2012). Development of Renewable Energy in Malaysia-Strategic Initiatives for Carbon Reduction in the Power Generation Sector. Procedia Engineering, 49, pp.384-391. 80. Silvestre, S. and Chouder, A. (2008). Effects of shadowing on photovoltaic module performance. Prog. Photovolt: Res. Appl., 16(2), pp.141-149. 81. Singh, J. (2014). Solar Radiation Measurements: Why Accuracy Matters. 1st ed. Available at: http://www.kippzonen.com/News/557/Solar-Radiation-Measurements-Why-Accuracy-Matters#.VlWtddKrTIU [Accessed 24 Nov. 2015]. 82. Solanki, C. (2013). Solar Photovoltaic Technology and Systems. Delhi: K.Ghosh, PHI Learning Private Limited, p.83. 83. Solangi, K., Islam, M., Saidur, R., Rahim, N. and Fayaz, H. (2011). A review on global solar energy policy. Renewable and Sustainable Energy Reviews, 15(4), pp.2149-2163. 84. Solar Electric System Design, Operation and Installation. (2009). 1st ed. Washington State University Extension Energy Program, pp.5-6. Available at: http://www.energy.wsu.edu/Documents/SolarPVforBuildersOct2009.pdf [Accessed 12 Oct. 2015]. 85. Solar Technologies Market Report 2010. (2011). 1st ed. Energy Efficiency &Renewable Energy. U.S Department of Energy. Available at: http://www.nrel.gov/docs/fy12osti/51847.pdf [Accessed 13 Oct. 2015]. 86. Sopian, K., Yigit, K., Liu, H., Kakaç, S. and Veziroglu, T. (1996). Performance analysis of photovoltaic thermal air heaters. Energy Conversion and Management, 37(11), pp.1657-1670. 87. Stapleton, G. and Neill, S. (2012). Grid-connected solar electric systems. Abingdon, Oxon: Earthscan. 88. Sulaiman, S.A., Hussain, H.H., Leh, N.S.H.N. and Razali, M.S., 2011. Effects of dust on the performance of PV panels. World Academy of Science, Engineering and Technology, 58, pp.588-593. 89. Sunlightelectric.com, (2015). History of Photovoltaics. Available at: http://www.sunlightelectric.com/pvhistory.php. [Accessed 22 Oct. 2015]. 90. Tsai, H., Tu, C. and Su, Y. (2008). Development of Generalized Photovoltaic Model Using MATLAB/SIMULINK. Proceedings of the World Congress on Engineering and Computer Science, WCECS, 2008, pp.1-6. 91. Verma, A. and Singhal, S., 2015. Solar PV Performance Parameter and Recommendation for Optimization of Performance in Large Scale Grid Connected Solar PV Plant—Case Study. Journal of Energy, 2(1), pp.40-53. 92. Vijayakumar, G., Kummert, M., Klein, S. and Beckman, W. (2005). Analysis of short-term solar radiation data. Solar Energy, 79(5), pp.495-504. 93. Villa, L., Picault, D., Raison, B., Bacha, S. and Labonne, A. (2012). Maximizing the Power Output of Partially Shaded Photovoltaic Plants Through Optimization of the Interconnections Among Its Modules. IEEE Journal of Photovoltaics, 2(2), pp.154-163. 94. Yoscovich, I. (2010). Shedding light on PV system shading. 1st ed. Solar Edge magazine. Available at: http://www.solaredge.com/articles/pv-system-shading [Accessed 27 Nov. 2015]. 95. Zeman, M. (2012). Photovoltaic Systems, in Solar Cells. pp.7-8. 96. Zhang, Q. (2013). Optimization and Design of Photovoltaic Micro-Inverter. Degree of Doctor of Philosophy. University of Central Florida.