THE IMPACT OF STUDENT PROGRESS FACTORS ON STUDENT PERSISTENCE IN E-LEARNING

Anna H. Lint

TUI University(Trident University International)

February 03, 2011

 

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AGENDA

AbstractIntroductionProblem Statement & Research Questions Literature ReviewResearch MethodologyData Analysis & Presentation of ResultsDiscussion & Implications of the Research

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ABSTRACT

The Purpose of the study : To evaluate and test the theoretical underpinnings of the Kember’s (1995)

student progress model that examines the direct or indirect effects of student persistence in e-learning.

Research Methodology * Sample population: 169 students at HCC * Instrument: DESP & SOAP / * Analysis: Logistic & Multiple Regression

Findings & Limitations * Significance: external attribution (Q13, AdQ13 /w CVs: Q13, AdQ13, Q14),

academic incompatibility (Q13) academic integration (Q14); GPA (partial mediation

effect) * Limitations: local cc, individual perception

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INTRODUCTION The knowledge gap

Student attrition theories: Spady (1971) Tinto (1975) Bean & Metzner (1985) Kember (1989) Kember (1995)

Kember’s (1995) student progress model predates the proliferation of online courses that occurred after the mid 1990s. Whether the Kember model fits with the current e-learning in a community college.

The significance of the study Student persistence is a critical issue in e-learning Relationships among student perceptions, learning styles, and

student persistence Application of Kember’s 1995 model to e-learning retention Future use for increasing student persistence.

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 PROBLEM STATEMENT

Dropout: 20 to 50 % of online students Online retention rate: 10 to 20 % lower than traditional class settings

Kember (1995) student progress modelDeveloped for distance education which was the early stage of

internet e-learning environmentLack of the association of student performance, cost-benefit analysis,

and student persistence

Measuring college student retention is complicated, confusing, and context dependentEvaluate direct or indirect effect among variables concerning student

persistence (Student progress factors, learning styles, student persistence)

Mixed results of Kember model

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RESEARCH QUESTIONS

1. Is there a statistically significant relationship between student perceptions

of the academic experience (a) social integration, (b) academic integration, (c) external attribution, and (d) academic incompatibility with student persistence (within the online learning environment at the community college level)? Does the relationship statistically significantly vary with respect to student characteristics and learning style?

2. Is there a statistically significant relationship between student perceptions of the academic experience (a) social integration, (b) academic integration, (c) external attribution, and (d) academic incompatibility with student persistence mediated by student performance (GPA)?

3. Is there a statistically significant relationship between student perceptions of the academic experience (a) social integration, (b) academic integration, (c) external attribution, and (d) academic incompatibility with student persistence mediated by cost-benefits?

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 LITERATURE REVIEW

Persistence in E-learning The relationship among the components of Kember’s

model: social and academic integration, external attribution, and academic incompatibility and their influence on persistence

The contributions of student characteristics and learning styles to persistence

The relationship between GPA and persistence Student cost-benefit analysis with persistence

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THEORETICAL ORIENTATION & CONCEPTUAL FRAME WORK

Figure 1. A model of student progress (Kember, 1995, p. 55)

  

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social integration

entry characteristics

external attribution

academic incompatibility

academic integration

GPA outcomecost/benefit

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THEORETICAL ORIENTATION & CONCEPTUAL FRAME WORK (Cont.)

Figure 2. Conceptual Framework

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DVPersistence

IVs(Student Perceptions)

Social integration

Academic integration

External Attribution

Academic incompatibility

Performance (GPA)

Cost-Benefit

CVsCharacteristics (Age, Gender, Delivery mode, Major, Work environment, Marital status, E-learning experience)

Learning Styles (VARK)

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RESEARCH METHODOLOGY

Research DesignPost-positivist worldview: reflects the need to identify

and assess the causes that influence outcomes Quantitative research using survey/ sample

population/ data collection and analysis

Sampling and PopulationPopulation: 169 community college students (out of a

sample of 800), Maryland : 21.1% return rateSample size: minimum 97, α = .05 with a medium ES (f 2=.15), power (.80)

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RESEARCH METHODOLOGY (Cont.)

Data Collection Upon the IRB approval, web based cross-sectional survey with target

students of summer 2010 at a community college, MD Data log, removal of error data, exporting to Excel Filtering data against research questions, importing data to SPSS 16.0

Instrumentation Section I: Student Characteristics Section II: Student Performance, Cost-Benefit analysis, and Student

Persistence Student Online Academic Persistence (SOAP) inventory

(cost-benefit & intent to persist: reliability of .84 & .68) Section III: Student Perceptions

Distance Education Student Progress (DESP) inventory (reliability of social integration: 0.69, external attribution: 0.77, academic integration: 0.80, and academic incompatibility: 0.76)

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RESEARCH METHODOLOGY (Cont.)

Statistical AnalysisTable 1: Research questions, Variables, and Statistical Analysis

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Research Questions IVs/CVs/MVs DV Statistics

1.Is there a statistically Student Perceptions Student Descriptive significant relationship (social integration, Persistence statisticsbetween student perceptions academic integration, (items 13,of the academic experience external attribution, 14 & 16/ Logistic(a)social integration academic categorical regression (b)academic integration incompatibility) & continuous(c)external attribution, and data) Multiple(d)academic incompatibility Covariant: regressionwith student persistence Characteristics (within the online learning (age, gender, environment at the community delivery mode, college level?) Does the major, work relationship statistically environment. significantly vary with marital status respect to student e-learning experience) characteristics and Learning Styleslearning style? (VARK)

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RESEARCH METHODOLOGY (Cont.)

Table 1. (Continued)

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2.Is there a statistically Student Perceptions Student Descriptive significant relationship (social integration, Persistence statisticsbetween student perceptions academic integration, (items 13,of the academic experience external attribution, 14 & 16/ Logistic(a)social integration academic categorical regression(b)academic integration incompatibility) & continuous(c)external attribution, and data) Multiple(d)academic incompatibility Mediator: regressionwith student persistence Student mediated by student Performance performance (GPA)? (measured by GPA)

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RESEARCH METHODOLOGY (Cont.)

Table 1. (Continued)

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3.Is there a statistically Student Perceptions Student Descriptivesignificant relationship (social integration, Persistence statisticsbetween student perceptions academic integration, (items 13, of the academic experience external attribution, 14 & 16/ Logistic(a)social integration academic categorical regression(b)academic integration incompatibility) & continuous (c)external attribution, and data) Multiple(d)academic incompatibility Mediator: regressionwith student persistence Cost-benefits mediated by cost-benefits?

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DATA ANALYSIS & PRESENTATION OF RESULTS

Descriptive Statistics : Sample: 169 students out of 800 (21.1% return rate), survey: 3 times

Student Characteristics Gender (F: 78.4%); Age (Over 23: 54.7%); Marital (S: 66.0%); Delivery Mode (O:

75.3%); Major: (S: 58.2%); Online Experience (Y: 56.2%); Work Environment (Static location: 84.4%)

Learning Style: V (42.6%); A (11.7%); R/W (30.2%); K (15.4%) GPA (M=3.0418, SD= .84562); Cost-benefits (M=3.0281, SD= .73098)

Student Persistence (Q13, Ad.Q13, & Q14) by Student PerceptionsTable 2. Mean scores of student persistence by student perceptions

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Q13 AdQ13 Q14

Social Int. 3.38 (SD: .061) 3.28 (SD: .063) 3.29 (SD: .109) Academic Int. 3.46 (SD: .045) 3.38 (SD: .047) 3.36 (SD: .090) External Att. 2.66 (SD: .051) 2.70 (SD: .052) 2.80 (SD: .094)Academic Inc. 2.95 (SD: .043) 2.94 (SD: .048) 2.95 (SD: .093)

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DATA ANALYSIS & PRESENTATION OF RESULTS (Cont.)

Bivariate Analysis

Table 3. Correlation between Ivs, CVs, & MVs and Student Percistence (Q13, AdQ13, & Q14)

Note. *p<.05, **p< .01. Spearman correlation was used for dichotomous variables. Pearson correlation was used

for continuous variables.

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Q13 AdQ13 Q14

Social Int. .168 .012 -.201*Academic Int. .276** .150 .273**External Att. -.290* -.216* -.248** GPA .197* .236* .248**Cost-benefits -.036 -.078* .003Age -.175* -.075 -.132 On-experience .219** .159 .236**

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DATA ANALYSIS & PRESENTATION OF RESULTS (Cont.)

Multivariate Analyses of Student Persistence

1. Research Question I Table 4. Regression Analysis Predicting the Variance of Student Perception,

Student characteristics, and Student Persistence

Note. OR was used for the probability of Q13 & AdQ13. β was used for the probability of Q14.

*p< .05, **p< .01.

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No control Control by CV Q13 / AdQ13 / Q14 (β) Q13 / AdQ13 / Q14 (β)

Academic Int. .269*External Att. .159** .213* .149* .135* -.768* Academic Inc. 3.796* Marital 13.124* On-experience 3.050* 3.472* .623*LS Auditory 15.196*

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DATA ANALYSIS & PRESENTATION OF RESULTS (Cont.)2. Research Question II

Baron and Kenny (1986) four steps for mediation: IV+DV(1) - IV+MV(2) - IV+MV+DV (3&4)

Sobel test for checking the complete mediation. Partial mediation effect of GPA to the relationship between S. Perceptions & S.

Persistence

Table 5. Regression Analyses for Student Perceptions and Student Persistence Mediated by GPA

Note. Logistic regression was used for Q13 & AdQ13. Multiple regression analysis was used for Q14.

3. Research Question III No mediation effect of Cost-benefits to the relationship between S. Perceptions & S.

Persistence

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No Mediation Mediated by GPA Q13 / AdQ13 / Q14 Q13 / AdQ13 / Q14

Academic Int. .895* .720*External Att. -1.837** -1.545* -1.930** -1.884**Academic Inc. 1.334* 1.406* GPA .744** .871** .385**

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DISCUSSION & IMPLICATIONS OF THE RESEARCH

Study Findings

External attribution had a significant relationship with Q13 & AdQ13.

External attribution had a significant relationship with Q13, AdQ13, &Q14 controlling with CVs.

Academic incompatibly had a significant relationship with Q13. Academic integration had a significant relationship with Q14. Prior online experience had a significant relationship with Q13

& Q14. Single & Auditory learners had a significant relationship with

AdQ13. A partial mediate effect of GPA and no mediate effect of Cost-

benefits for the relationship between student perception and student persistence 2/03/2011

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DISCUSSION & IMPLICATIONS OF THE RESEARCH (Cont.)

Research Question I Hypothesis of RQ1 is partially accepted and rejected. External attribution decreased in the odds of intent to enroll by a factor of .159 for

Q13/ .213 for AdQ13; Academic incompatibility increased by a factor of 3.796 for Q13; Academic integration was statistically significant on Q14.

Prior online: Q13 & Q14; Single: Ad.Q13; Auditory: AdQ13// External attribution: Q13, AdQ13, & Q14

Discussion & Suggestions Kember model: The importance of Social & Academic integration in distance education In this study: External attribution (negative) is dominant The harm of social networking for student persistence: Kord (2008), Hewitt (2003) Suggestions: 1. E-learning institutions need to understand the trend of students

2. Reducing External attribution such as social networking by reinforcing college website (user friendly) and providing time management course for multiple obligations

3. Increasing Academic integration by course related feedback. 4. Tailored courses for the Academic incompatibility (Intensive, regular, extended

course) 5. Prior online experience/ auditory learning style

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DISCUSSION & IMPLICATIONS OF THE RESEARCH (Cont.)

Research Question II The hypothesis of mediate effect of GPA on the relationship between S.

Perceptions & S. Persistence was partially accepted. A partial mediation effect of GPA.

Discussion and Suggestions GPA & persistence: Woosely (2009), Davis (2010) Direct effect of GPA and partial mediate effect Based on the results of RQ I, GPA can be increased by the academic

feedback and encouragement. Allow students flexible time line to understand the content of the course

Research Question III The hypothesis of mediate effect of Cost-benefits on the relationship

between S. Perceptions & S. Persistence was accepted. No mediation effect of Cost-benefit.

Cost-benefits & persistence: Tinto (1975), Strevy (2009), Stuart (2010) Yet the significant relationship among academic integration, academic

incompatibility, and Cost-benefits.

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DISCUSSION & IMPLICATIONS OF THE RESEARCH (Cont.)

Implication & Conclusion

Kember (1995) included external attribution and academic incompatibility as a harmful factor

External attribution was significant to most scores of student persistence: 1) Reflecting the current stream of e-learning environment such as IT &

Social network, 2) Time management for multiple obligations of students

Academic incompatibility & Academic integration: Tailored due date, flexible coursework, feedback, & encouragement of student performance

GPA: Encouragement of student performance by increasing appropriate academic environment

Prior online experience, Single, & Auditory learning style

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DISCUSSION & IMPLICATIONS OF THE RESEARCH (Cont.)

Recommendations & Limitations Recommendations for future research

Sample pools from 100% e-learning institutions Administrator, faculty, and staff perspective for student persistence Examine and compare the tailored programs for student persistence: intensive,

regular, and extended Three scores of student persistence in this study: compare, develop, or elaborate

the definition of student persistence.

Limitations Non-random sample of students matriculating in online and hybrid courses Some students might select the online courses without original intention Learning style was self-assessed by participants The DESP inventory is mainly focused on student perceptions, thus the lack of

identifying the institution's e-learning environment The study may not be generalized to other e-learning students in other locations

or having other values for their education

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