International Journal of Multidisciplinary Research and Growth Evaluation  |  ISSN (Online): 2582-7138  |  Double-Blind Peer Review  |  Open Access  |  CC BY 4.0

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     2026:7/3

International Journal of Multidisciplinary Research and Growth Evaluation

ISSN (Online): 2582-7138 | Open Access

Innovating Sustainability Education: Integrating Gamification and Deep Learning in Higher Education

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Volume: 6 | Issue: 1 | Pages: 328-338

Subject: Business

Country: Saudi Arabia

DOI: https://doi.org/10.54660/IJMRGE.2025.6.1.328-338

License: CC BY 4.0

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Abstract

This study explores the use of gamification and deep learning in higher education, examining how it might enhance student engagement, learning outcomes, and sustainability understanding. Evidence suggests that gamification tactics like points, badges, and leaderboards can make learning more engaging and entertaining while also increasing motivation and teamwork. Meanwhile, deep learning technologies enhance learning by tailoring lessons to each student's unique requirements and interests. Moreover, the study delves into how these methods might aid in the comprehension and implementation of sustainability principles, which are fundamental for solving world problems. Analyses of data obtained from 795 students showed that deep learning considerably individualizes learning and improves abilities, whereas gamification boosts engagement and motivation. Furthermore, the results demonstrate that by integrating various approaches, students can have a more comprehensive understanding of sustainability and be more motivated to incorporate sustainable activities into their everyday lives. The findings of this study show that new ways of teaching can help students succeed in school and provide them with the tools they need to build a greener world. The results of this study provide important information for schools and teachers who are trying to improve sustainability education by taking advantage of technological advancements.

How to Cite This Article

Mohammad Arif Riaz, Farooq Ebrahim, Muhammad Tariq Khan, Mohammad Ahsan Khan (2025). Innovating Sustainability Education: Integrating Gamification and Deep Learning in Higher Education . International Journal of Multidisciplinary Research and Growth Evaluation (IJMRGE), 6(1), 328-338. DOI: https://doi.org/10.54660/IJMRGE.2025.6.1.328-338

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  1. 2030. Thisresearchendeavortranscendsacademiaandrepresentsatransformativeeffortinsustainabilityeducation, carryingsignificanteffectsthatextendwellbeyondtheboundariesoftheuniversityclassroom. Furthersectionsofthisresearchwillexaminetheapproaches, results, anddiscoursethatshedlightonthepossibilitiesandobstaclesassociatedwiththeimplementationofgamificationanddeeplearningin Saudiuniversities. Thisisdonewiththeultimategoaloftransformingsustainabilityeducationandfurtheringtheinternationalcommitmenttoasustainablefuture. Review Literature Thisresearchisbuiltupon Self-Determination Theory(SDT\(Miller, Deci, and Ryan1988\[38citedby(Chiu2022\[10becauseitprovidesapertinentframeworkforourstudytitled"Revolutionising Sustainability Education: Integrating Gamificationand Deep Learningin Universities."SDTinvestigatestheinfluenceofmotivationonbehavior. Theinvestigationaidsincomprehendingtheimpactofbehaviorandlearning. Forinstance, gamificationmayincreaseintrinsicmotivationbyrenderinglearningentertaining, whereasdeeplearningmodelsmayaffectextrinsicmotivationthroughrewardsorrecognition. Studentengagementisanessentialmediatingvariable, and SDTenablesresearcherstoexaminehowstrategiesaffectstudents'perceivedcompetence, relatedness, andautonomy, thusaffectingtheirengagement. Inessence, SDTilluminatesthemotivationalandbehavioralaspectsoftheseadvancements, therebyprovidinginsightintotheirinfluenceontheengagement, learningoutcomes, motivation, andsustainablebehaviorofstudentswithinthecontextofhighereducation. Fig1: Conceptual Frameworkofthe Study International Journalof Multidisciplinary Researchand Growth Evaluationwww. allmultidisciplinaryjournal. com330|Page Gamification Strategies Gamificationstrategiesencompassapplyinggameelements, principles, andtechniquestonon-gamedomains, includingmarketing, education, andworkplacetasks, tostimulate, incentivize, andpromoteparticularbehaviorsamongparticipants. Theseapproachesareinfluencedbythegamingindustry, encompassingboardgames, videogames, andotherinteractiveformsofamusement, toenhancetheenjoymentandsatisfactionoftasksorprocedures(Garc?a-?lvarezetal.2022; Hakamietal.2023; Palaniappanand Noor2022\[16,22,
  2. 40. Researchhasindicatedthepotentialofgamificationtoincreasestudentmotivation, andengagementhasbeenextensivelypraised. Advocatescontendthatbyintegratinggameelementsintotheeducationalexperience, suchasleaderboards, points, andbadges, studentsaremoreinclinedtoengageactivelyintheirstudies. Thisincreasedengagementhasthepotentialtoresultinenhancedknowledgeretentionandimprovedlearningoutcomes(Rutledgeetal.2018\[
  3. 46. Gamificationhasgainedsignificantrecognitionasaneffectiveinstrumentinenhancingtheaccessibilityandenjoymentofintricatesubjects, includingsustainability. Ithasthepotentialtodismantleobstaclesandsimplifycomplextopics, whichmayleadtoincreasedcomprehensionandretentionamong(Aguilosand Fuchs2022; Chansand Portuguez Castro2021; Islamand Ali Khan2024b\[2,9,26gamificationprovidesimmediatefeedbackhencestudentscanmonitortheirprogress, pinpointareasthatrequiredevelopment, andmaintainmotivationtoaccomplishtheirlearninggoalsthroughtheuseofimmediatefeedback. Thegamificationteachingmethodaddresses UNSDG4, universalaccesstoequitable, high-qualityeducation. SDG4(Goal4|Departmentof Economicand Social Affairsn. d.\Specificallyaimstoprovideeducationalopportunitiestoindividualsresidingindevelopingcountriesorfacingphysicalortechnicalbarriersthatpreventthemfromattaininganeducation. Despiteseveralbenefits, severalresearchindicatedacritiqueofimplementinggamificationinacademia(Lim, Sanmugam, and Wan Yahaya2023; Shehawy, Khan, and Madkhali2024\[32,
  4. 54. Anadditionalchallengepertainstoonlinegamification, whichhighlightstheinadequatetechnologicalunderpinningsinvolvedindevelopingagamifiedenvironmentonlineduetoconnectivityproblemsandaccesslimitations. Intheinterim, thedilemmaofinteractionreferstothedifficultyofformingsocialconnectionsbasedonthevaryingpreferencesofpupilsregardingsynchronousorasynchronouslessons. Asaresult, educatorsareoftenconfrontedwiththedilemmaofwhetherornottoincorporategamificationintotheironlinecourses(Hung2017; Lim, Sanmugam, and Wan Yahaya2023\[23,
  5. 32. Deep Learning Model Theutilizationofdeepneuralnetworksandartificialintelligencemethodologiestooptimizeandtailortheeducationalexperience. Byutilizingthecapabilitiesofdeepneuralnetworks, deeplearningineducationenhanceshowstudentsgainknowledgeanddevelopskills(Kuppusamyand K2022; Suhluliand Ali Khan2022\[31,
  6. 55. Deep Learningisusedinacademicsforeducationaldataminingtoanalyzetheacademicperformanceofstudents. Thisprocesshasthepotentialtouncoverlatentinformationthatcouldbeutilizedtoenhancetheinstitution'scurrenteducationalsystem. Forexample, auniversitymayutilizeapredictivemodeltoforecastthefutureacademicperformanceofitsstudentstoidentifythosewhoareatriskofreceivinglowgrades. Thus, theuniversitycanencouragethemtoachievehigheracademicstandards, resultinginanoverallimprovementinstudentperformance(Islamand Ali Khan2024b; Prabowoetal.2021\[26,
  7. 42. Personalizedlearningisastrengthofdeeplearningmodels. Thesemethodsimproveengagementandunderstandingbytailoringcontenttostudents. Personalizedlearningexperiencesarebetteratfulfillinguniquestudentneedsandlearningstyles(Zhongetal.2020\[
  8. 60. Astudyby(Gazori, Rahbari, and Nickray2020\[17illustrateddeeplearningin Io Tdataprocessingreduceslatencyandexpenses(Suhluliand Ali Khan2022\[
  9. 55. Astudyconductedbyanagricultureuniversityillustrated Deeplearningisemployedtoimprovesustainabilityinagriculturethroughthepredictionofsoil, water, climateconditions, andcropvarieties(Ryoetal.2023\[
  10. 47. Astudyby(Jingetal.2023\[29mentionedthesignificanceof Learning Factoriesand Skill-Based Educationinattainingthe Sustainable Development Goalsofthe United Nations. Itemphasizestheimportanceofindustry-educationalcollaborationinthepreparationofacompetentlaborforceforsustainabledevelopment. Theresearchemphasizesthesignificanceofexperienceandknowledgeexchangeinthepursuitofamoresustainablefuture. The Norwegian Universityof Scienceand Technology(NTNU\usescost-effectivecommercialcomponentsandmicrocontrollerslikethemicro: bittogivestudentssustainableembeddedsystemdesignexpertise. Thisinnovativeteachingtechniqueemphasizes Visible Learningwiththeoreticallecturesandengaginggroupprojects, receivingexcellentcommentsfromstudentsandreferencegroups(Sanfilippoand Austreng2021\[
  11. 48. Student Learning Outcomes Inhighereducation, Student Learning Outcomes(SLOs\areessentialsincetheyoutlinewhatstudentsshouldknoworbeabletodofollowingcourseorprogramgraduation. Theseresultsguaranteecongruencewithworkforceneedsandacademicstandards, therebyguidingteaching, learning, assessment, andcurriculumdevelopment(Schoepp2019\[
  12. 50. Byincreasingengagement, motivation, andskilldevelopment, gamificationmixedwithdeeplearningtechniquescangreatlyincreasestudentlearningoutcomes; but, itsefficacycanbeaffectedbyseveralmoderatingvariables(Buckleyand Doyle2016\[
  13. 8. Withcustomizedanddeepgamificationtechniquesespeciallysuccessful, gamificationhasshownpromisetofavourablyaffectstudentlearningresults(Schofield2021\[
  14. 51. Still, oneshouldtakeintoaccountdifficultiesinexecutionandthenecessityofcomplexsolutionsdependingonpersonalqualities. Anotherfascinatingareaofresearchisthepartcognitiveprocessesandmotivationplayinthesuccessofgamificationanddeeplearningtechniques(Xiaoand Hew2024\[
  15. 58. Sustainability Knowledge Sustainabilityknowledgeisamultifacetedconceptessentialforaddressingcomplexglobalchallenges. Inenvironmentalscience, sustainabilityknowledgeconsistsmostlyoftheintegrationofsocial, organizational, andenvironmentalinformationtosolvechallengingsustainabilityissues. Knowledgeofsustainabilitygreatlyaffectsbusinessethicsandcorporatesocialresponsibility; itisalsoveryimportantforurbanplanninganddevelopmentandcanbeincludedinvariouslevelsofeducationtohelpcreateamoresustainable International Journalof Multidisciplinary Researchand Growth Evaluationwww. allmultidisciplinaryjournal. com331|Page(Lozanoetal.2022; Nolet2015; Papageorgiouetal.2024\[33,39,
  16. 41. Deeplearningtechniquesandgamificationhaveshowngreatpromiseinimprovingsustainabilityknowledgeandinvolvementinseveralbusinessandeducationalenvironments. Gamificationanddeeplearningstrategiesarepowerfultoolsforenhancingsustainabilityknowledge(Jainetal.2022\[27andengagement(Rinc?n-Flores, Mena, and Montoya2020\[43, leadingtoimprovedlearningoutcomes, increasedawareness(Zafaretal.2024\[59, andpositivebehavioralchanges. Ithasbeendemonstratedthatgamificationcanpowerfullycauselong-lastingbehaviormodificationtowardssustainability. Twolarge-scalefieldexperimentsshowedthatplayingasustainabilitygamegreatlyloweredfamilyelectricityuseandinspiredmoreattemptstosaveenergyandtheapparentvalueofsustainability. Usingthemediatingfunctionsofsustainableknowledgeandpsychologicalandsocialnorms, gamificationcaninduceusers'sustainabilityknowledgeandpro-sustainableintentions. Itenhancesconsumers'awarenessofsustainability, whichgreatlyaffectstheirintentionstowardsbeingpro-sustainable(Abou Kamaretal.2024\[
  17. 1. Sustainability Behaviour Sustainabilitybehavioristhebehaviorsandhabitspeopleorgroupsfollowtoreducetheirnegativeeffectsontheenvironmentandadvanceecologicalbalance. Thisideaissometimesexploredinpro-environmentalbehavior(PEB\, whichcomprisesparticularactsmeanttopreservethesurroundings(Gokilavanietal.2024; Medabeshand Khan2019; Tian, Zhang, and Li2020\[20,34,
  18. 56. Theethicalissuesinapplyinggamificationanddeeplearningtoaffectsustainabilitybehavior, theeffectofgamificationonsustainabilitybehaviorintheframeworkofdeeplearningstrategies, andtheanalysisandpredictionofsustainabilitybehaviorusingdeeplearningapproaches(Faisal Ali Khanand Ahmad2020\[
  19. 14. Thekeygamificationstrategiesusedtopromotesustainabilitybehaviorinvolveleveraginggamedesignprinciplestocreateengagingandmeaningfuluserexperiences(Schiele2018\[49, whiledeeplearningtechniquescanbeappliedtoanalyzeandpredictsustainabilitybehaviour(Mevoli, Leggett, and Davies2024\[
  20. 37. Theimpactofgamificationonsustainabilitybehaviorinthecontextofdeeplearningstrategieshasbeenshowntoinduceenduringbehaviorchangeanddrivepositivebehaviorchangeatscale(Dicheva, Irwin, and Dichev2019\[
  21. 12. However, theethicalconsiderationsinusinggamificationanddeeplearningtoinfluencesustainabilitybehaviorrequirefurtherexplorationandconsideration. Research Methodology In"Revolutionising Sustainability Education: Introducing Gamificationand Deep Learningin Universities,"thestudyapproachcentersonlookingathowdifferentpedagogicalandexperientiallearningapproachesmightsuccessfullyinspireentrepreneurialintentionsamongstudents. Theresearchersusedaquantitativemethod(Gill2020\[18tocompiledatafrombusinessstudentsatfiveinternationalinstitutionsselectedbasedontimesranking. Astructuredquestionnairemodifiedfrompaststudies. Selectedusingamixofstratifiedandintentionalsamplingtechniques, thesamplesoughttoreflectmanystudentviewpointsonentrepreneurshipeducation(Fielding, Lee, and Blank2016\[
  22. 15. Thestudyemphasizedontherelationshipbetweenseverallearningstrategiesandentrepreneurialgoalsusingstructuralequationmodeling(SEM\andconfirmatoryfactoranalysis(CFA\. Deeplearningmethodswereappliedtocreatesyntheticdatafromaninitial500responses, thereforeaugmentingthesamplesizeto795formorereliableanalysis. Testsofconvergentanddiscriminantvalidityaswellasadvancedstatisticalmodelslikethe Importance-Performance Map(IPM\thoroughlyvalidatedtheresultsandhighlightedwhichlearningactivities(Aguirre-Urretaand R?nkk?2018\[3includinggamificationhadthebiggestimpactonencouragingentrepreneurialobjectives. Incorporatingcutting-edgetechnologieslikegamificationanddeeplearning, theall-encompassingapproachemphasizesthededicationoftheresearchtofurtheringsustainabilityeducationinuniversityenvironments. Data Analysisand Interpretation Dataanalysisinthisstudywasdoneusingstructuralequationmodellingappliedwithpartialleastsquares(SEM-PLS\SEM-PLSwasusedsinceitisappropriateforanalysingintricateinteractionsbetweenseveralvariablesconcurrently, whichisnecessaryinthisinvestigationofhowvariousinstructionalapproachesaffectentrepreneurialinclinations. Handlingsmallersamplenumbersandnon-normaldatadistributions, SEM-PLSletsoneanalysebothdirectandindirecteffects. Italsoguaranteesthevalidityanddependabilityofthemeasuringmodels, thereforestrengtheningtheresultsoftheresearch. Measurement Model Usingthemeasurementmodelof SEM-PLS(Memonetal.2021\[36, latentvariables'observableindicatorsareevaluated, sodeterminethevalidityanddependabilityoftheconstructs. Reliabilityisguaranteedbyobtainingimportantconclusionsfromthestructuralmodeldependingonindicatorsproperlyreflectingtheunderlyinglatentvariables. Asshownin Figure2andfurtherdetailedin Tables1and2, themeasurementmodeliscrucialinconfirmingtheinternalconsistency, convergentvalidity, anddiscriminantvaliditysoguaranteeingthattheconstructsarewell-definedandmeasuredaccuratelybeforeanalyzingtherelationshipsbetweenvariablesinthestructuralmodel. International Journalof Multidisciplinary Researchand Growth Evaluationwww. allmultidisciplinaryjournal. com332|Page Fig1: Measurement Model Table1: Convergent Validity Constructs Cronbach'salpha Compositereliability(rho_a\Compositereliability(rho_c\Averagevarianceextracted(AVE\Gamification&Deep Learning Strategies0.9090.9100.9300.688 Student Learning Outcomes0.7500.8220.8590.677 Sustainability Behaviour0.8540.8620.9320.872 Sustainability Knowledge0.8250.8280.8950.740 Table1showshowthestudyevaluatedconvergentvalidityusingvariousimportantparameters. Acrossallconstructions, theresultsshowgreatinternalconsistencyanddependability. Cronbach'salphafor"Gamification&Deep Learning Strategies"was0.909; withanaveragevarianceextracted(AVE\of0.688, thecompositereliabilityvalues(rho_a=0.910, rho_c=0.930\clearlyshowahighdegreeofdependability, soindicatingthattheitemsfairlycapturetheunderlyingconstruct. Withan AVEof0.677,"Student Learning Outcomes"showedsimilarsoliddependability(Cronbach'salpha=0.750, compositereliability=0.859\. Reflectinggreatconvergentvalidity,"Sustainability Behavour"displayedoutstandinginternalconsistencywith Cronbach'salphaof0.854, ahighcompositereliabilityof0.9932, andan AVEof0.
  23. 872. Finally, withan AVEof0.740,"Sustainability Knowledge"alsoshowngreatdependability(Cronbach'salpha=0.825, compositereliability=0.895\. Thesefindingsgenerallysupportthevalidityanddependabilityofthechosenstudyconstructsforadditionalinvestigation. Table2: Discriminant Validity Constructs Gamification&Deep Learning Strategies Student Learning Outcomes Sustainability Behaviour Sustainability Knowledge Gamification&Deep Learning Strategies0.830 Student Learning Outcomes0.7660.823 Sustainability Behaviour0.7450.6280.934 Sustainability Knowledge0.7210.7790.7220.860 Table2showstheresultsofdiscriminantvalidity, evaluatingtheuniquenessofeveryconceptfromtheothers. Tovalidatediscriminantvalidity, thediagonalvalueswhichreflectthesquarerootofthe Average Variance Extracted(AVE\foreveryconstructshouldbegreaterthanthecorrelationswithotherconstructs. Thesquarerootof AVEfor"Gamification&Deep Learning Strategies,"0.830, ishigherthanthatofitsrelationshipswiththeotherconstructions. Allofthesearegreaterthantheirinter-constructcorrelations,"Student Learning Outcomes"hasasquarerootof AVEof0.823;"Sustainability Behaviour"has0.99; and"Sustainability Knowledge"has0.
  24. 860. Thesefindingsprovidegooddiscriminantvalidityinthemodelbyconfirmingthattheconstructionsdifferfromoneanother. International Journalof Multidisciplinary Researchand Growth Evaluationwww. allmultidisciplinaryjournal. com333|Page Table3: Effect Size(f?\and Coefficientof Determination(R?\Values Constructs F-square Constructs R-square R-squareadjusted Gamification&Deep Learning Strategies->Student Learning Outcomes1.419 Student Learning Outcomes0.5870.586 Gamification&Deep Learning Strategies->Sustainability Behaviour1.247 Sustainability Behaviour0.5550.554 Gamification&Deep Learning Strategies->Sustainability Knowledge1.084 Sustainability Knowledge0.520.52 Table's3representsf-squarevalues,"Gamification&Deep Learning Strategies"'effectsizeontheothermodelconstructions. Consideredaminorinfluenceifanf-squarevalueexceeds0.02; beyond0.15, amediumeffect; above0.35, asignificanteffect. Here"Gamification&Deep Learning Strategies"hasasignificantinfluenceon"Student Learning Outcomes"(f-square=1.419\,"Sustainability Behaviour"(f-square=1.247\, and"Sustainability Knowledge"(f-square=1.084\. Thesehighf-squarevaluesshowthatgamificationanddeeplearningapproachesdramaticallyaffectstudentresults, sustainabilitybehavior, andsustainabilityknowledge, thereforehighlightingtheirimportantpartinimprovingentrepreneurialandsustainabilityeducation. Foreverydependentconstruct, thetable's R-squarevaluesshowthepercentageofvarianceexplainedbytheindependentvariable,"Gamification&Deep Learning Strategies."Withan R-squarescoreof0.587for"Student Learning Outcomes,"themodelexplains58.7%ofthevarianceintheseoutcomes. Reflectingthestabilityofthemodel, themodified R-squareof0.586showslittlechange. Withanadjusted R-squareof0.554,"Sustainability Behaviour"hasan R-squareof0.555, thereforeexplaining55.5%ofthevariancewiththemodel. The R-squarefor"Sustainability Knowledge"is0.52, meaningthemodelexplains52%ofthevariance; theadjusted R-squareshowsnochange. Thesenumbersshowthatforallthreeconstructs, themodelhasasignificantexplainingability. Table4: Multicollinearityand Model Fit Indices Items VIFSaturatedmodel Estimatedmodel GDLS12.783SRMR0.0840.103GDLS22.994d_ULS0.7351.116GDLS33.554d_G0.4780.575GDLS43.26 Chi-square2128.592356.574GDLS52.305NFI0.760.735GDLS61.865SB12.249SB22.249SK11.985SK21.875SK31.771SLO12.615SLO22.903SLO31.221 Table4offersanumberofimportantmetricsaboutmodelfitfortheconstructsandmulticollinearity. Thefactthatthe Variance Inflation Factor(VIF\valuesforeveryitemarebelow5indicatesthatthereisnotmulticollinearityinthemodel. Themodelfitindicescontrasttheestimatedmodeltherealmodelwiththesaturatedmodel, amodelwithperfectfit. Bothneartotheacceptablethresholdof0.08, the Standardised Root Mean Square Residual(SRMR\valuesforthesaturatedmodelandtheestimatedmodelare0.084and0.103respectively, thereforeshowingafairfit. Whiletheestimatedmodelhassomewhathigherdiscrepancies(d_ULS=1.116, d_G=0.575\, thed_ULSandd_Gvalues, whichevaluatethedifferencebetweenthemodels, pointtolowervaluesforthesaturatedmodel(d_ULS=0.735, d_G=0.478\. Thoughlowervaluesareusuallydesirable, the Chi-squarevalues2128.59forthesaturatedmodeland2356.574fortheestimatedmodelshowagoodfit. Lastbutnotleast, the Normed Fit Index(NFI\values(0.735fortheestimatedmodeland0.76forthesaturatedmodel\areneartotheadvisedthresholdof0.90, therebyindicatingthemodelisadequatebutmightbeimprovedinfit. Basedontheseindices, themodelshowsratherdecentvalidityandfitoverall. Table5: MVDescriptive Indicators Items Mean Median Observedmin Observedmax Standarddeviation Excesskurtosis Skewness Numberofobservationsused Cram?r-von Misesteststatistic Cram?r-von Misespvalue GDLS13.4054151.355-0.995-0.4227954.6470.000GDLS23.3074151.397-1.121-0.3837954.9410.000GDLS33.4784151.232-0.823-0.3827954.5070.000GDLS43.4784151.187-0.73-0.3707954.5720.000GDLS53.6394151.153-0.113-0.7387956.2840.000GDLS63.3093151.161-0.767-0.3037954.8830.000SB13.233151.232-0.937-0.2797954.7310.000SB23.1963151.214-0.841-0.1907954.1020.000SK13.3033151.118-0.588-0.1857955.010.000 International Journalof Multidisciplinary Researchand Growth Evaluationwww. allmultidisciplinaryjournal. com334|Page SK23.2623151.178-0.841-0.1677954.2630.000SK33.2543151.2-0.812-0.1797954.2010.000SLO13.2863151.242-0.708-0.4677955.3370.000SLO23.3263151.181-0.621-0.3677954.7190.000SLO33.544151.059-0.548-0.3117955.360.000 Thetable6presentsdescriptivestatisticsforvariousconstructs, including Gamification&Deep Learning Strategies(GDLS\Sustainability Behaviour(SB\, Sustainability Knowledge(SK\, and Student Learning Outcomes(SLO\, basedonresponsesfrom795participants. Themeanscoresformostitemsrangefrom3.3to3.5ona5-pointscale, indicatingthatrespondentstendtoeitheragreeorfeelneutralaboutthestatements. Themedianscoresareconsistentlyaround3or4, suggestingthatmanyparticipantsratedtheirexperiencespositively. Thedataalsoshowsafullrangeofresponses, withminimumvaluesof1andmaximumvaluesof
  25. 5. Thestandarddeviations, whichvarybetween1.05and1.4, indicatemoderatevariabilityintheresponses. Additionally, thekurtosisvaluesarenegativeforallitems, reflectingflatterdistributionsthanwhatwewouldexpectinanormaldistribution, whiletheslightlynegativeskewnesssuggestsatendencytowardhigherratings. Finally, the Cram?r-von Misestestresultsshowp-valuesof0forallitems, indicatingsignificantdeviationsfromnormality, althoughthesedeviationsareminor. Overall, thestatisticsrevealthatresponsesarerelativelyevenlyspreadbutleanslightlytowardagreement, withonlysmalldeviationsfromanormaldistribution. Structural Model In SEM, orstructuralequationmodelling, thestructuralmodelshowstheexpectedrelationshipsbetweenlatentcomponentsintheresearch. Itallowstheresearchofbothdirectandindirecteffectsbyshowinghowtheindependentfactorsinfluencethedependentvariables. Byuseofstructuralmodelanalysis, researcherscanassessthedegreeandrelevanceoftheseconnections, thereforeprovidingvaluablefreshviewpointsonthebasicmechanismsproducingtheobservedevents. Fig1: Structural Model Table6: Effect Testing Originalsample(O\Samplemean(M\Standarddeviation(STDEV\Tstatistics(|O/STDEV|\Pvalues Gamification&Deep Learning Strategies->Student Learning Outcomes0.7660.7660.01647.2450.000 Gamification&Deep Learning Strategies->Sustainability Behaviour0.7450.7450.01840.7550.000 Gamification&Deep Learning Strategies->Sustainability Knowledge0.7210.7210.02133.6700.000 Table6representsthemainoutcomes Student Learning Outcomes(SLO\, Sustainability Behaviour(SB\, and Sustainability Knowledge(SK\showcasingtheeffectsofgamificationanddeeplearningstrategies(GDLS\Thefindingsshowthateveryoneofthesedomainsbenefitsmuchfrom GDLS. Withanestimateof0.766, forexample, theimpacton Student Learning Outcomesisverynoteworthysincethesetechniquesclearlyhelpstudentsgreatly. Comparatively, theinfluenceon Sustainability Knowledgestandsat0.721; theeffecton Sustainability Behaviourisassessedat0.
  26. 745. Theveryhigh T-statisticsandap-valueof0.000indicatethateveryrelationshipisratherimportant. Theseresultsunderlinehowincludinggamificationanddeeplearningtechniquesnotonlyimprovesstudents'learning International Journalof Multidisciplinary Researchand Growth Evaluationwww. allmultidisciplinaryjournal. com335|Pageenvironmentsbutalsoencouragessustainabilitybehaviorandknowledgeofsustainability. Discussion Thefindingsofthesurveyshowagenerallyfavorableviewofgamificationanddeeplearninginimprovingstudents'participationandknowledgeofenvironmentalissues(Islamand Ali Khan2024a; Medabeshand Khan2020\[25,
  27. 35. Withameanof3.405, respondentsjudgedtheiragreementwithgamificationcomponentsimprovingengagementinlearningactivitiessuggestingthatsuchcharacteristicssomewhatincreaseengagement. Likewise, themeanscoreof3.307forthepersonalizingprovidedbydeeplearningtechnologiesreflectsaviewthatthesetechnologiescanmeetindividuallearningrequirements. Givenameanof3.478, immediatefeedbackviagamificationwasjudgedcrucialfortrackingimprovement. Moreover, studentsshowedconfidencethattheseapproachesimprovetheirknowledgeofsustainability; thiswasalsoshownbyameanscoreof3.478fortheconvictioningamification'scapacitytosimplifysustainabilitytopics. Ahighermeanof3.639showsthatgamifiedlearningenvironmentswereidentifiedforfosteringteamworkwhichisalignedwiththeprevioisstudies(Shehawy, Khan, and Madkhali2024\[
  28. 54. Whilestudentsfeltgamificationraisedmotivationandenjoyment(meanof3.309\, confidenceinreachinglearninggoalswassomewhatlower, withameanof3.
  29. 230. Withameanof3.196, thesupposedcontributionofgamificationtoskilldevelopmentshowsamodestconfidenceinitsefficiency. Withameanof3.302, studentsshowedamoreskepticalattitudeonthereflectionofactualcomprehensioningamifiedtests, though. Theseapproachesproducedknowledgeaboutsustainabilityprinciplesassessedat3.262; awarenessofglobalsustainabilityissuesratedsomewhathigherat3.
  30. 286. Complyingwithsustainablepracticesandadoptingbehavioursthatsupportsustainabilityscoredmeansof3.326and3.540, respectively, thereforedemonstratingthefavorableinfluenceofeducationonenvironmentalactions(Shehawyand Ali Khan2024\[53 Thoughtherearedifferencesinperspectiveacrossmanyfacets, theresultsimplythatgamificationanddeeplearninggreatlyaffectstudents'engagement, understanding, andcommitmenttosustainability. Conclusion Thepresentresearchemphasisesthetransformingpossibilitiesofincludinggamificationanddeeplearningtechnologyintosustainabilityeducationincolleges. Theresultsimplythatbothapproachesgreatlyimprovestudentinvolvement, drive, andcomprehensionofdifficultsustainabilityissues. Whiledeeplearningtechnologiesprovidetailoredlearningexperiencesthatmeetindividualneeds, gamificationthroughpoints, badges, andleaderboardsincreasesengagementandpromotesteamwork. Thesestrategiestakentogetherhelpstudentstoretainknowledge, growtheirskills, andinspirethemtoapplysustainabilityprinciplesinpracticalsettings. Notwithstandingthedifficultiestechnicalconstraintsanddifferentstudentpreferencesforengagement, amongotherthingsgamificationanddeeplearninghaveshownfavourableoveralleffectsonstudentlearningoutcomesandsustainabilityknowledge. Thesecreativeapproacheshelptodeepenknowledgeofsustainabilitybymakinglearningmoreinteractiveandcustomised, therebyinspiringbehaviourthatfitswithworldsustainabilitytargets. Teachersshouldthinkabouthoningthesestrategiesgoingaheadtomeetobstaclesandmaximisetheircapacitytotransformsustainabilityteaching. Implicationof Study Forinstitutionsstrivingtoimprovesustainabilityeducationaswellasforteachers, thisstudyhasmajorramifications. Universitiesmaydevelopmoreinteresting, customised, andsuccessfullearningenvironmentsbyincludinggamificationanddeeplearningtechnologies. Thismethodnotonlyincreasesstudentinvolvementandmotivationbutalsohelpsthemtograspsustainabilityideasandpromotesactualimplementationofsustainableactivities. Theseinstrumentsallowteacherstobettermatchtheirapproacheswiththedemandsoftheirstudents, thereforeproducingagenerationmorededicatedtotacklingworldwideenvironmentalissues. Annexure IItems Mean Median Observedmin Observedmax Standarddeviation Excesskurtosis Skewness Numberofobservationsused Cram?r-von Misesteststatistic Cram?r-von Misespvalue1 Towhatextentdoyouagreethatgamificationelements(suchaspoints, badges, orleaderboards\hanceyourengagementinlearningactivities?3.4054151.355-0.995-0.4227954.6470.0002 Howeffectivelydoyoufeeldeeplearningtechnologiespersonalizeyourlearningexperiencebasedonyourindividualneedsandpreferences?3.3074151.397-1.121-0.3837954.9410.0003 Howimportantisimmediatefeedback(throughgamification\inhelpingyoumonitoryourprogressinyourlearningjourney?3.4784151.232-0.823-0.3827954.5070.0004 Doyoubelievethatgamificationanddeep3.4784151.187-0.73-0.377954.5720.000 International Journalof Multidisciplinary Researchand Growth Evaluationwww. allmultidisciplinaryjournal. com336|Pagelearningcaneffectivelyenhanceyourunderstandingofsustainabilityconcepts?5 Howeffectivelydogamifiedlearningenvironmentspromotecollaborationandsocialinteractionamongstudents?3.6394151.153-0.113-0.7387956.2840.0006 Towhatextentdoyoufindgamificationelementsincreaseyourenjoymentandmotivationinthelearningprocess?3.3093151.161-0.767-0.3037954.8830.0007 Howconfidentareyouinyourabilitytoachievethelearningoutcomessetforyourcourseduetogamifiedlearningstrategies?3.233151.232-0.937-0.2797954.7310.0008 Towhatextentdoyoufeelthatgamificationhascontributedtoyourskilldevelopmentinthesubjectmatter?3.1963151.214-0.841-0.197954.1020.0009 Howwelldoyouthinkgamifiedassessmentsreflectyouractualunderstandingandcompetenciesinthesubjectarea?3.3033151.118-0.588-0.1857955.010.00010 Towhatextentdoyoufeelknowledgeableaboutsustainabilityconceptsasaresultofgamifiedanddeeplearningmethods?3.2623151.178-0.841-0.1677954.2630.00011 Howoftendoyouapplyyourunderstandingofsustainabilityconceptsinreal-worldscenariosafterengagingwithgamifiedlearning?3.2543151.2-0.812-0.1797954.2010.00012 Howawareareyouofcurrentglobalsustainabilitychallengesasaresultofyoureducation?3.2863151.242-0.708-0.4677955.3370.00013 Howfrequentlydoyouengageinbehaviorsthatpromotesustainability(e. g., recycling, conservingenergy\asaresultofyoureducationalexperiences?3.3263151.181-0.621-0.3677954.7190.00014 Towhatextenthasyoureducationinfluencedyourcommitmenttosustainablepracticesinyourdailylife?3.544151.059-0.548-0.3117955.360.000 References
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Journal: International Journal of Multidisciplinary Research and Growth Evaluation (IJMRGE)

Publisher: Anfo Publication House

ISSN: 2582-7138 (Online)

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Language: English

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