不同类型教学交互对大学生深度学习影响研究-基于结构方程模型的分析

不同类型教学交互对大学生深度学习影响研究——基于结构方程模型的分析Title:AnInvestigationintotheImpactofDifferentTypesofInstructionalInteractionsonUniversityStudents'DeepLearning:AStructuralEquationModelingAnalysisIntroduction:Inrecentyears,thefieldofeducationhasrecognizedtheimportanceofinteractiveinstructionalapproachesinpromotingdeeplearningamonguniversitystudents.Deeplearninggoesbeyondsurface-levelmemorizationandaimstodevelopcriticalthinking,problem-solving,andanalyticalskills.Thisstudyaimstoinvestigatetheinfluenceofdifferenttypesofinstructionalinteractionsonuniversitystudents'deeplearning,usingastructuralequationmodeling(SEM)analysis.TheoreticalFramework:1.DeepLearning:Deeplearningreferstoatransformativeapproachthatencouragesstudentstoactivelyconstructknowledge,integrateconcepts,anddevelopadeeperunderstandingofthesubjectmatter.Itinvolvescriticalthinking,problem-solving,reflectivelearning,andawillingnesstochallengeassumptions.2.InstructionalInteractions:Instructionalinteractionsrefertotheexchangeofinformationandideasbetweenstudentsandinstructorsorpeers.Differenttypesofinstructionalinteractionsincludeinstructor-centered,student-centered,andpeerinteractions.3.StructuralEquationModeling:SEMisastatisticaltechniqueusedtoanalyzestructuralrelationshipsbetweenobservedandlatentvariables.Itallowsfortheexaminationofcomplexrelationships,includingdirectandindirecteffects,betweenvariables.Methods:1.Sample:Arepresentativesampleofuniversitystudentsfromvariousdisciplineswillbeselectedtoparticipateinthestudy.2.DataCollection:Datawillbecollectedusingastructuredquestionnairethatmeasuresstudents'perceptionsofinstructionalinteractionsanddeeplearning.Thequestionnairewillbedesignedbasedonexistingvalidatedscales.3.MeasurementModel:Aconfirmatoryfactoranalysis(CFA)willbeconductedtovalidatethemeasurementmodel.Thisanalysiswillensurethattheobservedvariablesadequatelyrepresentthelatentvariables.4.StructuralModel:TheSEManalysiswillexaminetherelationshipsbetweeninstructionalinteractionsanddeeplearning.Themodelwillincludedirectpathsbetweeninstructionalinteractionsanddeeplearning,aswellasindirectpathsmediatedbyothervariablessuchasmotivationandengagement.ResultsandDiscussion:Thefindingsofthestudycanprovidevaluableinsightsintotheimpactofdifferenttypesofinstructionalinteractionsonuniversitystudents'deeplearning.TheSEManalysiswillrevealthedirectandindirecteffectsofinstructionalinteractionsondeeplearning,allowingforacomprehensiveunderstandingoftheunderlyingmechanismsatplay.Implications:1.EducationalPractice:Thestudy'sfindingscaninformeducationalpractitionersonthedesignandimplementationofeffectiveinstructionalstrategiesthatpromotedeeplearningamonguniversitystudents.2.PolicyImplications:Theresearchmayhavewiderimplicationsforeducationalpoliciesandcurriculumdevelopment,highlightingtheimportanceofincorporatinginteractiveinstructionalapproachesintohighereducationsystems.LimitationsandFutureResearch:Thisstudyisnotwithoutlimitations.Theuseofaself-reportquestionnairemayintroduceresponsebias.Additionally,thestudymaynotcapturethefullextentofinstructionalinteractionsexperiencedbystudents.Futureresearchcanutilizemixed-methodsapproachesanddirectobservationstoprovideamorecomprehensiveunderstandingoftheimpactofdifferenttypesofinstructionalinteractionsondeeplearning.Conclusion:Thisstudyaimedtoinvestigatetheimpactofdifferenttypesofinstructionalinteractionsonuniversitystudents'deeplearningusingastructuralequationmodelinganalysis.Thefindingswillcontributetotheexistingliteratureoninstructionalinteractionsanddeeplearning,providinginsightsthatcaninformeducationalpracticeandpolicy.Byunderstandingthemechanismsbywhichinstruct