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The study targets the effect of a neural network artificial intelligence approach on achievement and long-term retention. Forty students participated, with five showing 60%–80% misconceptions before the intervention. Posttest results show statistically significant differences from pretest, and learning connections are associated with the input and first hidden layers.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/predicting-learning-outputs-and-retention-through-neural-network-artificial-intelligence-in-photosynthesis-transpiration-and-translocation/232793/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/predicting-learning-outputs-and-retention-through-neural-network-artificial-intelligence-in-photosynthesis-transpiration-and-translocation/232793.png","ImageObject",300,407,{"name":92,"@type":93},"Patrick","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-10",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",15,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What research design is used to predict learning outcomes and retention?","Question",{"text":113,"@type":114},"A pretest-posttest single-group experimental design is used to investigate misconceptions and predict learning outcomes and retention.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How many students participated, and how were misconceptions assessed?",{"text":118,"@type":114},"Forty students participated. Five students were identified as having 60%–80% misconceptions in a misconception test before the neural network artificial intelligence approach.",{"name":120,"@type":111,"acceptedAnswer":121},"What do the results indicate about learning and the neural network structure?",{"text":122,"@type":114},"The mean posttest score is statistically significant compared with the mean pretest score, and the input layer and first hidden layer are related to the artificial intelligence output.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},232793,1789073665,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},549758146520,"https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470","Predicting learning outputs and retention through neural network artificial intelligence in photosynthesis, transpiration and translocation  \nAnanta Kumar JENA  \nDepartment of Education, Assam University Silchar-788011, Assam, INDIA  \nEmail: [akjenaaus@gmail.com](akjenaaus@gmail.com)  \n[Received 12 Aug.](Received 12 Aug.), 2017  \nRevised 27 Jan., 2018  \nContents  \n􀁸 Abstract  \n􀁸 Introduction  \n􀁸 Methodology  \n􀁸 Analysis and Results  \n􀁸 Findings and Discussion  \n􀁸  Conclusion  \n􀁸 References   \n􀁸 Appendix  \nAbstract  \nArtificial Intelligence is a branch of computer science connects, classifies, differentiates, and elaborates the domains of learning in neural network, a paradigm shift is using in the construction of knowledge. In this pretest-posttest single group experimental design, neural network artificial intelligence used to investigate the existing misconception status of the participants, and predicted the learning  \noutcomes, and retention of learning. The study aimed to assess the effects of neural network artificial intelligence approach on the achievement and retention in science learning. Forty students of a class were participated in this study, and out of them five students found having 60% to 80% of misconceptions assessed in themisconception test before exposed to the neural network artificial intelligence approach. It resulted that the mean of posttest score was statistically significant indifferent from the mean of the pre test score. It was resulted that input layer and first hidden layer were related with the output of the artificial intelligence.  \nKeywords: Artificial intelligence, neural network, photosynthesis, translocation, transpiration  \nIntroduction  \nArtificial Intelligence (AI) is a branch of computer science relates, connects, classifies, differentiates, and elaborates different cognitive domains in the neurological network. John Mc. Carthy coined the term in 1955 and viewed, “it is the science and engineering of making intelligent machines.” AI is highly technical, specialized, and based on deduction, reasoning, and problem solving techniques linked with mathematics, reasoning, logic, and engineering to identifying the concept in various situations. The central theme of AI research includes reasoning, planning, learning, communicating, perceiving, constructing knowledge (Jena, 2012), and manipulating the objects or concepts. This field includes human creativity, and intelligence to stimulate the cognition (Goyache, 2001). Recently, statistical methods and computational intelligence is included in AI for mathematical optimization and ontological classification while neural network stimulates human brain and intelligence, and in fact, the nature of mind stimulates to perform better or to give an appropriate response.  \nNow a day, online learning approaches are using to enhance science learning performance (Jena, 2014, 2015), but AI a new paradigm shift increases the levels of learners’ intelligence (Guastello & Rieke, 1994) . Human intelligence is based on intuition, common sense, judgments, creativity, beliefs and ability to demonstrate the intelligence by communicating effectively through reasoning and critical thinking those stimulate human behaviour and cognitive process (Jolly et al. 2007) . It needs  \nhuman expertise for quick data entry, manipulation, accumulation, and assimilation of information to find a better result and long retention. AI is not a mixed knowledge but a developed knowledge while human intelligence is a mixed knowledge, is not able to write a huge amount of data in memory. In the 19th century, French scientist, and philosopher Claude Bernard argued,“we achieve more than we know, we know more than we understand and we understand more than we can explain”. AI sometimes contrasts with conventional computing process. It uses to search the matching code in the environment to decode the human intelligence but it’s just like the software process needs inputs, short term memory, a","cbCaihZhp49EqPtV","https://ap.wps.com/l/cbCaihZhp49EqPtV","pdf",1010509,26,"English","# Abstract\n# Introduction\n# Methodology\n# Analysis and Results\n# Findings and Discussion\n# Conclusion\n# References\n# Appendix","[{\"question\":\"What research design is used to predict learning outcomes and retention?\",\"answer\":\"A pretest-posttest single-group experimental design is used to investigate misconceptions and predict learning outcomes and retention.\"},{\"question\":\"How many students participated, and how were misconceptions assessed?\",\"answer\":\"Forty students participated. Five students were identified as having 60%–80% misconceptions in a misconception test before the neural network artificial intelligence approach.\"},{\"question\":\"What do the results indicate about learning and the neural network structure?\",\"answer\":\"The mean posttest score is statistically significant compared with the mean pretest score, and the input layer and first hidden layer are related to the artificial intelligence output.\"}]","Predicting Learning Outputs and Retention Through Neural Network Artificial Intelligence in Photosynthesis, Transpiration and Translocation | PDF",66]