This document summarizes key findings from a path analysis study examining how instructional technology affects student learning. The analysis included over 1,000 undergraduate students. It identified four main variables: 1) IT academic use, 2) course learning management, 3) IT value added for learning, and 4) academic performance (GPA). The path analysis found that course management had the largest effect on improving learning. SAT scores were the strongest predictor of GPA. The analysis provides evidence that IT can have value added benefits for student learning when integrated into academic use and course management.
The document summarizes a meta-analysis exploring the effects of shared cognitive measures on team performance. It outlines 6 shared cognitive constructs examined: shared mental models, team mental models, information sharing, transactive memory systems, cognitive consensus, and group learning. It then lists the research questions and data collection methods used to analyze articles measuring these constructs and their relationship to team performance.
This document discusses an experience-based approach to mathematics education called MEBATM. It focuses on both routine and nonroutine problem solving. Routine problems involve known procedures while nonroutine problems emphasize heuristics. The document also introduces the Mathematics Pentathlon® program which features strategic games to develop diverse mathematical thinking and active nonroutine problem solving skills.
1. An Erp Performance Measurement Framework Using A Fuzzy Integral ApproachDonovan Mulder
This document proposes a seven-step framework for measuring the performance of an enterprise resource planning (ERP) system using a fuzzy integral approach. The framework aims to link performance indicators (PIs) to the objectives of the ERP implementation project. A fuzzy ERP performance index is used to account for ambiguities in evaluating ERP system performance. An example application in Taiwan demonstrates how the proposed framework can be applied in practice.
Martin E. Sandler, Ph.D., Research In Higher Education Article, October 2000Martin Sandler
This document summarizes a research study that developed and tested an integrated model of student persistence for adult undergraduate students. The model built upon previous models of student retention and attrition, and introduced three new variables: career decision-making self-efficacy, perceived stress, and financial difficulty. Data from over 900 adult students at two-year and four-year colleges was analyzed using structural equation modeling. The results showed that career decision-making self-efficacy had the strongest influence on factors related to student persistence like academic integration, institutional commitment, and intent to persist. This highlights the importance of career development for understanding adult student retention.
Mary Jones receives an initial written warning for poor work performance from her supervisor Tom Doe. The warning cites issues with timely processing of travel requests, incomplete travel forms, untimely filing, and ignoring instructions regarding office organization. Mary is urged to improve her performance before further corrective action.
How To Write a Letter of Explanation to the IRS. From Success Tax Relief.Success Tax Relief
How to write a letter of explanation to the IRS regarding an inquiry sent to you from the experts at Success Tax Relief. For more articles on taxes and dealing with the IRS, visit www.successtaxrelief.com
The document summarizes a meta-analysis exploring the effects of shared cognitive measures on team performance. It outlines 6 shared cognitive constructs examined: shared mental models, team mental models, information sharing, transactive memory systems, cognitive consensus, and group learning. It then lists the research questions and data collection methods used to analyze articles measuring these constructs and their relationship to team performance.
This document discusses an experience-based approach to mathematics education called MEBATM. It focuses on both routine and nonroutine problem solving. Routine problems involve known procedures while nonroutine problems emphasize heuristics. The document also introduces the Mathematics Pentathlon® program which features strategic games to develop diverse mathematical thinking and active nonroutine problem solving skills.
1. An Erp Performance Measurement Framework Using A Fuzzy Integral ApproachDonovan Mulder
This document proposes a seven-step framework for measuring the performance of an enterprise resource planning (ERP) system using a fuzzy integral approach. The framework aims to link performance indicators (PIs) to the objectives of the ERP implementation project. A fuzzy ERP performance index is used to account for ambiguities in evaluating ERP system performance. An example application in Taiwan demonstrates how the proposed framework can be applied in practice.
Martin E. Sandler, Ph.D., Research In Higher Education Article, October 2000Martin Sandler
This document summarizes a research study that developed and tested an integrated model of student persistence for adult undergraduate students. The model built upon previous models of student retention and attrition, and introduced three new variables: career decision-making self-efficacy, perceived stress, and financial difficulty. Data from over 900 adult students at two-year and four-year colleges was analyzed using structural equation modeling. The results showed that career decision-making self-efficacy had the strongest influence on factors related to student persistence like academic integration, institutional commitment, and intent to persist. This highlights the importance of career development for understanding adult student retention.
Mary Jones receives an initial written warning for poor work performance from her supervisor Tom Doe. The warning cites issues with timely processing of travel requests, incomplete travel forms, untimely filing, and ignoring instructions regarding office organization. Mary is urged to improve her performance before further corrective action.
How To Write a Letter of Explanation to the IRS. From Success Tax Relief.Success Tax Relief
How to write a letter of explanation to the IRS regarding an inquiry sent to you from the experts at Success Tax Relief. For more articles on taxes and dealing with the IRS, visit www.successtaxrelief.com
This document summarizes a journal article that proposes an e-Learning Maturity Model (eMM) as a framework to help institutions assess and improve their e-learning capabilities. The eMM is designed to assess an institution's ability to develop, deploy, and support e-learning. It draws from similar maturity models used in software engineering to benchmark processes. Implementing an eMM could provide institutions a roadmap to guide improvements and allow them to benchmark their e-learning capabilities against other institutions.
M-Learners Performance Using Intelligence and Adaptive E-Learning Classify th...IRJET Journal
This document discusses using machine learning classification algorithms to predict student performance based on educational data. It compares the performance of five classification algorithms - J48, Naive Bayes, Bayes Net, Backpropagation Network, and Radial Basis Function Network - in predicting student academic achievement using attributes like demographic information, test scores, and academic factors. The experiment found that the Radial Basis Function Network algorithm achieved the highest accuracy, correctly classifying 100% of instances, compared to 75-95% accuracy for the other algorithms. Convolutional neural networks are also discussed as a powerful tool for image and language processing in educational data mining.
New Fuzzy Model For quality evaluation of e-Training of CNC Operatorsinventionjournals
The quality of e-learning is a very important issue, especially when production technologies are concerned. This paper introduces a new fuzzy model for e-learning quality evaluation. All uncertainties and consequent imprecision are modeled by triangular fuzzy numbers. The quality of CNC e-learning process is determined by using the fuzzy logic IF-THEN rules. The proposed method derives an aggregated satisfaction value both for the participants as well as the trainers.The authors introduce a genuine metric interval for the objective evaluation of E-learning effect. The OLS regression model estimates the magnitude and polarity of Elearning effect on participants` perception of the training quality. The predicted coefficient of E-learning effect on the overall quality of CNC training is estimated to be14.88 measurement points with a negative impact on overall satisfaction. These novel findings shed a new light on the quantitative effect of E-learning on CNC machine training and contribute to the contemporary scientific literature within the research area.The developed model is illustrated by real-life data from secondary technological schools from central Serbia
ANALYSIS OF STUDENT ACADEMIC PERFORMANCE USING MACHINE LEARNING ALGORITHMS:– ...indexPub
Student academic performance is the great value of institutes, universities and colleges. All colleges majorly focus on the career development of students. The academic performance of students plays a vital role in the establishment of a bright career. On the basis of better academic performance, the placement of the students will be better and the same will be reflected in the form of better admission and future. Machine learning can be deployed for the prediction of student performance. Various algorithms are playing an important role in the prediction of the accuracy of various machine learning models. These articles discuss various algorithms that can be helpful to deploy for predicting student academic performance. The article discusses various methods, predictive features and the accuracy of machine learning algorithms. The primary factors used for predicting students performance are academic institution, sessional marks, semester progress, family occupation, methods and algorithms. The accuracy level of various machine learning algorithms is discussed in this article.
This document discusses a study analyzing student satisfaction with various instructional technology techniques. The study surveyed 215 students enrolled in 4 undergraduate business courses about their satisfaction with commonly used IT tools like presentation software, email/discussion lists, word processing, web search engines, online libraries, and web development applications. The study aimed to determine the relationship between different types and degrees of IT used and student satisfaction, as well as the impact of IT techniques on student perceptions of enhanced classroom behaviors like student-student and student-instructor interaction, increased information and quality of instruction, and improved course organization. The sample was predominantly male (68%), aged 19-26, and majoring in general business (75%).
IRJET- Using Data Mining to Predict Students PerformanceIRJET Journal
This document describes a study that used logistic regression to predict student performance based on educational data. The researchers collected student data including exam scores, attendance, study hours, family income, etc. from a large dataset. Logistic regression achieved the best prediction accuracy of 82.03% compared to other models like naive bayes, K-nearest neighbor, and multi-layer perceptron. The results indicate that around 230 students would perform poorly, 600 would perform fairly, and 200 would perform well based on the predictive model. This analysis can help identify students needing extra support and help universities improve academic outcomes.
IRJET- Performance for Student Higher Education using Decision Tree to Predic...IRJET Journal
This document discusses using decision trees to predict career decisions for 12th grade students in India. It first provides background on the challenges in the Indian education system and how data mining can help improve decision making. It then reviews previous studies applying various data mining techniques like decision trees and random forests to predict student performance. The paper proposes using a decision tree approach on student data to distinguish slow and fast learners and help students make better career choices based on their interests and skills. The decision tree approach achieved 80% accuracy in predicting student career decisions, helping students choose appropriate paths.
The quasimoderating effect of perceived affective quality on an extending Tec...alabrictyn
This document discusses an empirical study that tests an extended Technology Acceptance Model (TAM) to understand factors influencing learner acceptance of the WebCT e-learning system. The study incorporates additional constructs of perceived affective quality (PAQ), flow, perceived usefulness (PU), and perceived ease of use (PEOU) to predict behavioral intention to use WebCT. Structural equation modeling is used to analyze relationships between constructs and test hypotheses. Results support that PU, PEOU, and flow positively impact intention to use WebCT, and that PAQ has a moderating effect on the relationships in the extended TAM.
Automated Essay Score Predictions As A Formative Assessment ToolLisa Muthukumar
This document discusses an automated essay scoring feature added to ETIPS cases, which are online learning objects designed to develop teachers' instructional decision-making skills about technology integration. The summary evaluates students' initial responses to the automated essay scorer to understand their reactions, inform future implementation, and provide insight to improve the reliability of the scorer. Research suggests learning environments should provide formative assessment to give students feedback and opportunities to improve, and automated scoring holds promise as a formative assessment tool within online learning. Student perceptions of similar computer-based formative assessment have been generally positive.
New Fuzzy Model for quality evaluation of E-Training of CNC Operatorsinventionjournals
This document proposes a new fuzzy model for evaluating the quality of e-learning training for CNC operators. It begins by discussing the importance of continuous education in production technologies like CNC. It then reviews existing literature on evaluating e-learning quality and identifies uncertainties in criteria weights and values. The document goes on to introduce assumptions of the proposed model which uses fuzzy set theory to represent uncertainties. Criteria weights are determined using fuzzy AHP based on linguistic assessments from trainers. An example application evaluates participant satisfaction with e-learning training quality. Finally, regression analysis estimates the isolated effect of e-learning on training quality perceptions.
This document summarizes a proposed research study that will investigate secondary school students' acceptance of the i-Teacher e-Learning system based on the Technology Acceptance Model (TAM). The study will survey 500 secondary students who use the i-Teacher system, which incorporates a pedagogical agent, for 6 months. Students will then complete a questionnaire measuring their perceived ease of use, perceived usefulness, attitude, behavioral intentions, and actual use of the system. The researchers expect strong, positive correlations between the TAM variables and that the findings will provide evidence for implementing learning management systems in secondary schools.
The International Journal of Mechanical Engineering Research and Technology is an international online journal published Quarterly offers fast publication schedule whilst maintaining rigorous peer review. The use of recommended electronic formats for article delivery expedites the process All submitted research articles are subjected to the immediate rapid screening by editors consultation with Editorial Board or others working in the field of appropriate to ensure that they are likely to be the level of interest and importance of appropriate for the journal.
ISSN 2454-535X
International Journal of Mechanical Engineering Research and Technology aims to provide the best possible service to authors of original research articles, and the fairest system of peer review.
This document discusses using machine learning to predict student performance in online learning environments. It reviews studies that have examined online course data to predict student outcomes using machine learning techniques. The studies identified features of online courses used for prediction, outputs of prediction models, methodologies for feature extraction, evaluation metrics, and challenges. Machine learning algorithms commonly used in the studies include logistic regression, naive Bayes, decision trees, AdaBoost, k-nearest neighbor, and neural networks. The document provides an in-depth analysis of different machine learning models and their effectiveness in predicting student certificate acquisition, grades, and students at risk of failure.
Improving the quality of e-learning courses in Higher EducationSusana Lemos
The document discusses a study analyzing student satisfaction in e-learning courses to identify factors that influence satisfaction and areas for improvement. It administered an online questionnaire to 33 students in an e-learning master's program in Portugal. The questionnaire measured satisfaction across 9 dimensions. Results showed overall favorable satisfaction levels, with highest satisfaction for the curricular program and faculty/tutors dimensions. Support services had the lowest satisfaction. The study aims to provide guidelines to improve dimensions like support services and workload distribution to enhance e-learning course quality.
1. The study evaluated an evaluation questionnaire used to assess safety management system training delivered by an Australian rail corporation using immersive simulation.
2. An analysis of the questionnaire found that it measured participant reactions to the training but did not accurately assess whether the intended learning objectives were achieved. The questionnaire did not have validated scales and items loaded inconsistently onto factors.
3. While participants generally reacted positively to the immersive simulation training, with over 75% finding previous simulations and 85% finding current simulations helpful, the evaluation tool requires revision to properly measure learning outcomes as intended by the training program.
The University of Pennsylvania Models of Excellence program encourages excellence, provides inspiring role models for emulation, and recognizes innovative staff accomplishments that reflect initiative, leadership, collaboration, increased efficiency, and a deep commitment to service.
Factors influencing the adoption of e learning in jordanAlexander Decker
This document summarizes a study that examines factors influencing the adoption of e-learning in Jordan. It develops an extended Technology Acceptance Model called TAM-EL that includes perception of usefulness, ease of use, degree of support (patronised), and previous experience (practised) as factors influencing attitudes toward e-learning adoption. The study surveyed 380 students to test this model. It found that attitude contributes 57% to predicting e-learning adoption, while degree of support contributes 28%. The findings recommend more determined engagement of users to increase e-learning adoption.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document summarizes a journal article that proposes an e-Learning Maturity Model (eMM) as a framework to help institutions assess and improve their e-learning capabilities. The eMM is designed to assess an institution's ability to develop, deploy, and support e-learning. It draws from similar maturity models used in software engineering to benchmark processes. Implementing an eMM could provide institutions a roadmap to guide improvements and allow them to benchmark their e-learning capabilities against other institutions.
M-Learners Performance Using Intelligence and Adaptive E-Learning Classify th...IRJET Journal
This document discusses using machine learning classification algorithms to predict student performance based on educational data. It compares the performance of five classification algorithms - J48, Naive Bayes, Bayes Net, Backpropagation Network, and Radial Basis Function Network - in predicting student academic achievement using attributes like demographic information, test scores, and academic factors. The experiment found that the Radial Basis Function Network algorithm achieved the highest accuracy, correctly classifying 100% of instances, compared to 75-95% accuracy for the other algorithms. Convolutional neural networks are also discussed as a powerful tool for image and language processing in educational data mining.
New Fuzzy Model For quality evaluation of e-Training of CNC Operatorsinventionjournals
The quality of e-learning is a very important issue, especially when production technologies are concerned. This paper introduces a new fuzzy model for e-learning quality evaluation. All uncertainties and consequent imprecision are modeled by triangular fuzzy numbers. The quality of CNC e-learning process is determined by using the fuzzy logic IF-THEN rules. The proposed method derives an aggregated satisfaction value both for the participants as well as the trainers.The authors introduce a genuine metric interval for the objective evaluation of E-learning effect. The OLS regression model estimates the magnitude and polarity of Elearning effect on participants` perception of the training quality. The predicted coefficient of E-learning effect on the overall quality of CNC training is estimated to be14.88 measurement points with a negative impact on overall satisfaction. These novel findings shed a new light on the quantitative effect of E-learning on CNC machine training and contribute to the contemporary scientific literature within the research area.The developed model is illustrated by real-life data from secondary technological schools from central Serbia
ANALYSIS OF STUDENT ACADEMIC PERFORMANCE USING MACHINE LEARNING ALGORITHMS:– ...indexPub
Student academic performance is the great value of institutes, universities and colleges. All colleges majorly focus on the career development of students. The academic performance of students plays a vital role in the establishment of a bright career. On the basis of better academic performance, the placement of the students will be better and the same will be reflected in the form of better admission and future. Machine learning can be deployed for the prediction of student performance. Various algorithms are playing an important role in the prediction of the accuracy of various machine learning models. These articles discuss various algorithms that can be helpful to deploy for predicting student academic performance. The article discusses various methods, predictive features and the accuracy of machine learning algorithms. The primary factors used for predicting students performance are academic institution, sessional marks, semester progress, family occupation, methods and algorithms. The accuracy level of various machine learning algorithms is discussed in this article.
This document discusses a study analyzing student satisfaction with various instructional technology techniques. The study surveyed 215 students enrolled in 4 undergraduate business courses about their satisfaction with commonly used IT tools like presentation software, email/discussion lists, word processing, web search engines, online libraries, and web development applications. The study aimed to determine the relationship between different types and degrees of IT used and student satisfaction, as well as the impact of IT techniques on student perceptions of enhanced classroom behaviors like student-student and student-instructor interaction, increased information and quality of instruction, and improved course organization. The sample was predominantly male (68%), aged 19-26, and majoring in general business (75%).
IRJET- Using Data Mining to Predict Students PerformanceIRJET Journal
This document describes a study that used logistic regression to predict student performance based on educational data. The researchers collected student data including exam scores, attendance, study hours, family income, etc. from a large dataset. Logistic regression achieved the best prediction accuracy of 82.03% compared to other models like naive bayes, K-nearest neighbor, and multi-layer perceptron. The results indicate that around 230 students would perform poorly, 600 would perform fairly, and 200 would perform well based on the predictive model. This analysis can help identify students needing extra support and help universities improve academic outcomes.
IRJET- Performance for Student Higher Education using Decision Tree to Predic...IRJET Journal
This document discusses using decision trees to predict career decisions for 12th grade students in India. It first provides background on the challenges in the Indian education system and how data mining can help improve decision making. It then reviews previous studies applying various data mining techniques like decision trees and random forests to predict student performance. The paper proposes using a decision tree approach on student data to distinguish slow and fast learners and help students make better career choices based on their interests and skills. The decision tree approach achieved 80% accuracy in predicting student career decisions, helping students choose appropriate paths.
The quasimoderating effect of perceived affective quality on an extending Tec...alabrictyn
This document discusses an empirical study that tests an extended Technology Acceptance Model (TAM) to understand factors influencing learner acceptance of the WebCT e-learning system. The study incorporates additional constructs of perceived affective quality (PAQ), flow, perceived usefulness (PU), and perceived ease of use (PEOU) to predict behavioral intention to use WebCT. Structural equation modeling is used to analyze relationships between constructs and test hypotheses. Results support that PU, PEOU, and flow positively impact intention to use WebCT, and that PAQ has a moderating effect on the relationships in the extended TAM.
Automated Essay Score Predictions As A Formative Assessment ToolLisa Muthukumar
This document discusses an automated essay scoring feature added to ETIPS cases, which are online learning objects designed to develop teachers' instructional decision-making skills about technology integration. The summary evaluates students' initial responses to the automated essay scorer to understand their reactions, inform future implementation, and provide insight to improve the reliability of the scorer. Research suggests learning environments should provide formative assessment to give students feedback and opportunities to improve, and automated scoring holds promise as a formative assessment tool within online learning. Student perceptions of similar computer-based formative assessment have been generally positive.
New Fuzzy Model for quality evaluation of E-Training of CNC Operatorsinventionjournals
This document proposes a new fuzzy model for evaluating the quality of e-learning training for CNC operators. It begins by discussing the importance of continuous education in production technologies like CNC. It then reviews existing literature on evaluating e-learning quality and identifies uncertainties in criteria weights and values. The document goes on to introduce assumptions of the proposed model which uses fuzzy set theory to represent uncertainties. Criteria weights are determined using fuzzy AHP based on linguistic assessments from trainers. An example application evaluates participant satisfaction with e-learning training quality. Finally, regression analysis estimates the isolated effect of e-learning on training quality perceptions.
This document summarizes a proposed research study that will investigate secondary school students' acceptance of the i-Teacher e-Learning system based on the Technology Acceptance Model (TAM). The study will survey 500 secondary students who use the i-Teacher system, which incorporates a pedagogical agent, for 6 months. Students will then complete a questionnaire measuring their perceived ease of use, perceived usefulness, attitude, behavioral intentions, and actual use of the system. The researchers expect strong, positive correlations between the TAM variables and that the findings will provide evidence for implementing learning management systems in secondary schools.
The International Journal of Mechanical Engineering Research and Technology is an international online journal published Quarterly offers fast publication schedule whilst maintaining rigorous peer review. The use of recommended electronic formats for article delivery expedites the process All submitted research articles are subjected to the immediate rapid screening by editors consultation with Editorial Board or others working in the field of appropriate to ensure that they are likely to be the level of interest and importance of appropriate for the journal.
ISSN 2454-535X
International Journal of Mechanical Engineering Research and Technology aims to provide the best possible service to authors of original research articles, and the fairest system of peer review.
This document discusses using machine learning to predict student performance in online learning environments. It reviews studies that have examined online course data to predict student outcomes using machine learning techniques. The studies identified features of online courses used for prediction, outputs of prediction models, methodologies for feature extraction, evaluation metrics, and challenges. Machine learning algorithms commonly used in the studies include logistic regression, naive Bayes, decision trees, AdaBoost, k-nearest neighbor, and neural networks. The document provides an in-depth analysis of different machine learning models and their effectiveness in predicting student certificate acquisition, grades, and students at risk of failure.
Improving the quality of e-learning courses in Higher EducationSusana Lemos
The document discusses a study analyzing student satisfaction in e-learning courses to identify factors that influence satisfaction and areas for improvement. It administered an online questionnaire to 33 students in an e-learning master's program in Portugal. The questionnaire measured satisfaction across 9 dimensions. Results showed overall favorable satisfaction levels, with highest satisfaction for the curricular program and faculty/tutors dimensions. Support services had the lowest satisfaction. The study aims to provide guidelines to improve dimensions like support services and workload distribution to enhance e-learning course quality.
1. The study evaluated an evaluation questionnaire used to assess safety management system training delivered by an Australian rail corporation using immersive simulation.
2. An analysis of the questionnaire found that it measured participant reactions to the training but did not accurately assess whether the intended learning objectives were achieved. The questionnaire did not have validated scales and items loaded inconsistently onto factors.
3. While participants generally reacted positively to the immersive simulation training, with over 75% finding previous simulations and 85% finding current simulations helpful, the evaluation tool requires revision to properly measure learning outcomes as intended by the training program.
The University of Pennsylvania Models of Excellence program encourages excellence, provides inspiring role models for emulation, and recognizes innovative staff accomplishments that reflect initiative, leadership, collaboration, increased efficiency, and a deep commitment to service.
Factors influencing the adoption of e learning in jordanAlexander Decker
This document summarizes a study that examines factors influencing the adoption of e-learning in Jordan. It develops an extended Technology Acceptance Model called TAM-EL that includes perception of usefulness, ease of use, degree of support (patronised), and previous experience (practised) as factors influencing attitudes toward e-learning adoption. The study surveyed 380 students to test this model. It found that attitude contributes 57% to predicting e-learning adoption, while degree of support contributes 28%. The findings recommend more determined engagement of users to increase e-learning adoption.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
1. IT as a Value Added Component of Teaching and Learning: Excerpts from a Path Analysis
Martin E. Sandler, Ph.D., Assistant Director, Assessment, TLTC, Seton Hall University
Abstract: Student attitudes and perceptions of the use of instructional technology are mapped in a structural equation model derived from survey data and an instrument
developed in part on the principles of undergraduate practice of Chickering & Gamson (1999) and the capacity of computer environments as a tool for enhancing learning
(Azevedo 2005). IT Academic Use, Course Learning Management, and IT as a Value Added component of teaching and learning are focally examined.
Introduction and Conceptual Framework Excerpts from a path analysis are focally examined including Discussion and Conclusion
IT Academic Use (R2 = .158) with eight total effects, Course
A path analytic procedure was conducted on survey data yielding a Managements Improves Learning, (R2 = .584) with five total effects, By employing path analysis derived from reliable measures,
structural equation model as a means to map the experience of and IT Value Added for learning (R2 = .195), the criterion dependent assessment can assist practitioners and faculty in understanding the role of
undergraduate learners using technology. The survey examined how variable in the model, with six total effects (see Figure 1). Total IT as a value added component of undergraduate education. Path analysis
technology affected learning inside and outside the classroom and was in part effects are assumed to be causal such that the cause precedes the empowers the assessment professional with skills to address the call for
based on the principles of undergraduate practice of Chickering & Gamson effect in direction and temporal order. accountability made by accrediting bodies with reliable measures. In this
(1999) and the capacity of computer environments as a tool for enhancing instance clear evidence was obtained about the focal variables examined
learning (Azevedo 2005). Accordingly, the largest total effect arose from students’ and in particular of IT as a value added component of academic life.
Math and Verbal SAT Score AT .859 (p < .001), that is, for every unit
A structural model was determined from measurement data to increase in Cumulative GPA there is a corresponding eighty-six (86) Importance and Relevance to Other Institutions
explore the impact of technology on Academic Performance (Cumulative GPA), percent increase in students’ SAT scores. The SAT proved to be a very By mapping and tracing effects using path analysis, structural
IT Academic Usage, Course Learning Management, and IT Value Added, four strong contributor/predictor of the explained variance of Cumulative equation modeling enables assessment professionals, instructional designers,
focal variables in a path analysis that included twenty-four (24) variables. For GPA totaling seventy-nine (79 )percent. and faculty to explore survey data through a powerful new lens close to the
ease of understanding the reader may interchange the meaning of IT to respondents’ experience,
represent Information Technology or Instructional Technology. thereby explaining elements of
GENDER teaching and learning with
Methodology, Sample and Data Reduction X1
technology with greater clarity.
ETHNICITY/ The findings confirm the
Eleven hundred fifty-two (1152) undergraduate students were RACE
- .120
Endogenous ongoing importance of the
Variables
X2 IT USE NON -
included in an on-line survey administration during the spring 2008 semester. IT SKILL LEVEL
ACADEMIC
.317 Y5 .119***
COURSE MGMT.
principles of Chickering &
PARENTS’
With a forty-four (44) percent online survey response rate, the sample for EDUCATION 2
Y1 R2 = 0.114
.261 IMPROVES Gamson (1999) and the utility
AL LEVEL X3 R = 0.078
analysis included (N=509). LEARNING/Bb
2
of computer environments as a
.665 Y9 R = 0.584
HOUSEHOLD
.104
.666 .212 .228
tool for enhancing learning
INCOME X4 .179
After a principal components analysis of the survey data, eleven .081*** (Azevedo 2005).
CLASSROOM .072 .081***
reliable measures were determined with coefficients between .68 and .89. PREFERRED IT DISTRACTS/ MEDIA IMPRS.
.163
.497
.079***
Subsequently, twenty-four (24) variables were included in the path analysis LEVEL IT IN
COURSE X5
IMPEDES
LEARNING Y2
- .112
LEARNING Y6
2 - .108
.085 References
R = 0.246
that included twelve (12) endogenous; eleven arose from the subscale factors R2 = 0.001 .443 IT TEAM
IT-VALUE
addressed. Cumulative GPA was added as the twelfth endogenous variable; VERBAL & -.133 COORDINATION - .173
ADDED Azevedo, R. (2005).
MATH SAT Y12
twelve (12) exogenous variables denoting student background were included. X6
.072
Y10
.062** R2 = 0.195 “Computer Environments as
R2 = 0.047
ACADEMIC
- .391
- .436**
.162
.212 .505 MetacognitiveTools for
SATISFACTION .331
ASPIRATION
WITH WIRELESS Enhancing Learning,”
Structural Equation Modeling X7 IT USE
ACADEMIC
.308 NETWORK Y7 Educational Psychologist,
Y3 R2 = 0.027
Descriptive, transformational, and inferential statistics were
YEARS TO
COLLEGE R2 = 0.158
- .296 40(4), 193-197.
DEGREE X8 .108 CUMULATIVE
.253
obtained using SPSS 16. Structural Equation Modeling (SEM) using a weighted - .164 .165
GRADE POINT
least squares (WLS) estimator followed by employing LISREL 8.80 after a CUMULATIVE .859 AVERAGE - GPA b Chickering, A.W. and
HOURS The conventional syntax used in
Y11 R2 = 0.785
pretreatment phase with PRELIS 2.50. As a simple indication of Model Fit, the PASSED X9
DIVERSE
path diagrams may be deviated Gamson, Z.F. (1999).
- .162 from in order to simplify
ratio of Chi-Square / degrees of freedom = 50.335/193 = 0.261 providing TALENTS Y8
representation.
“Development and
HOURS SATISFACTION R2 = 0.035
evidence of a fine fit; ratios below 2.00 are recognized as very good. STUDY
X10
WITH LAPTOP Adaptations of the Seven
Y4 Principles for Good Practice in
R2 = 0.008 Chi-Square with 193 degrees of freedom = 50.335 (p = 1.000). All total effects represented
Findings HOURS
EMPLOYED are significant atp <.001 with the exception of those marked * at p <.01, ** at p < .02, Undergraduate Education,”
X11 and *** at p < .05 ; total effects <.060 are trimmed and not represented. A dashed line New Directions for Teaching
Total effects are mapped in the course of a Path Analysis. Six (6) out represents a non-significant effect. The figure presented serves as a final structural model.
and Learning, no. 80, Winter
HOUSING/
of twelve (12) endogenous variables of the structural model had notable levels Exogenous 1999.
COMMUTING
Figure 1: 2008 Teaching and Learning with Technology Survey
of variance explained, between eleven (11) and seventy-nine (79) percent. In X12
Variables
Model: Total Effects on Six Principal Endogenous Variables b
addition, a robust number, ninety-eight (98) percent of the hypotheses 24_var_T&LwT_2008LIS _9 a_cent _EM_Centered_3_BEST.LS8
explored were confirmed. The total effects on three focal endogenous variables
in a structural equation model are exclusively featured. Martin E. Sandler, Ph.D. has several years experience as a researcher, faculty member and administrator in higher education. He has published in
Research in Higher Education and the Journal of College Student Development and is currently Assistant Director, Assessment, TLTC, Seton Hall University.