Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Intelligent tutoring systems and learning performance

Intelligent tutoring systems and learning performance Intelligent tutoring systems (ITS) are a supplemental educational tool that offers great benefits to students and teachers. The systems are designed to focus on an individual’s characteristics, needs and preferences in an effort to improve student outcomes. Despite the potential benefits of such systems, little work has been done to investigate the impact of ITS on users. To provide a more nuanced understanding of the effectiveness of ITS, the purpose of this paper is to explore the role of several ITS parameters (i.e. knowledge, system, service quality and task–technology fit (TTF)) in motivating, satisfying and helping students to improve their learning performance.Design/methodology/approachData were obtained from students who used ITS, and a structural equation modeling was deployed to analyze the data.FindingsData analysis revealed that the quality of knowledge, system and service directly impacted satisfaction and improved TTF for ITS. It was found that TTF and student satisfaction with ITS did not generate higher learning performance. However, student satisfaction with ITS did improve learning motivation and resulted in superior learning performance. Data suggest this is due to students receiving constant and constructive feedback while simultaneously collaborating with their peers and teachers.Originality/valueThis study verifies that there was a need to assess the benefits of ITS. Based on the study’s findings, theoretical and practical implications are proposed. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Online Information Review Emerald Publishing

Intelligent tutoring systems and learning performance

Download PDF
17 pages

Loading next page...
 
/lp/emerald-publishing/intelligent-tutoring-systems-and-learning-performance-T0q1UvN37v
Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
1468-4527
DOI
10.1108/oir-11-2017-0340
Publisher site
See Article on Publisher Site

Abstract

Intelligent tutoring systems (ITS) are a supplemental educational tool that offers great benefits to students and teachers. The systems are designed to focus on an individual’s characteristics, needs and preferences in an effort to improve student outcomes. Despite the potential benefits of such systems, little work has been done to investigate the impact of ITS on users. To provide a more nuanced understanding of the effectiveness of ITS, the purpose of this paper is to explore the role of several ITS parameters (i.e. knowledge, system, service quality and task–technology fit (TTF)) in motivating, satisfying and helping students to improve their learning performance.Design/methodology/approachData were obtained from students who used ITS, and a structural equation modeling was deployed to analyze the data.FindingsData analysis revealed that the quality of knowledge, system and service directly impacted satisfaction and improved TTF for ITS. It was found that TTF and student satisfaction with ITS did not generate higher learning performance. However, student satisfaction with ITS did improve learning motivation and resulted in superior learning performance. Data suggest this is due to students receiving constant and constructive feedback while simultaneously collaborating with their peers and teachers.Originality/valueThis study verifies that there was a need to assess the benefits of ITS. Based on the study’s findings, theoretical and practical implications are proposed.

Journal

Online Information Review – Emerald Publishing

Published: Jul 30, 2019

Keywords: Intelligent tutoring system; IS success model; Learning motivation; Learning performance; Satisfaction; Task–technology fit

References

  • The beginning of a beautiful friendship? Intelligent tutoring systems and MOOCs
    Aleven, V.; Sewall, J.; Popescu, O.; Xhakaj, F.; Chand, D.; Baker, R.; Gasevic, D.
  • Analyzing learner language: towards a flexible natural language processing architecture for intelligent language tutors
    Amaral, L.; Meurers, D.; Ziai, R.
  • The impact of aesthetics on employee satisfaction and motivation
    Bjerke, R.; Ind, N.; De Paoli, D.
  • Advisor–student relationship in business education project collaborations: a psychological contract perspective
    Bordia, S.; Hobman, E.V.; Restubog, S.L.D.; Bordia, P.
  • Web-based education for all: a tool for development adaptive courseware
    Brusilovsky, P.; Eklund, J.; Schwarz, E.
  • Key drivers of passenger loyalty: a case of Frankfurt–Istanbul flights
    Calisir, N.; Basak, E.; Calisir, F.
  • Decision support system (DSS) use and decision performance: DSS motivation and its antecedents
    Chan, S.H.; Song, Q.; Sarker, S.; Plumlee, R.D.
  • Learning through playing virtual age: exploring the interactions among student concept learning, gaming performance, in-game behaviors, and the use of in-game characters
    Cheng, M.T.; Lin, Y.W.; She, H.C.
  • Examining the integrated influence of fairness and quality on learners’ satisfaction and web-based learning continuance intention
    Chiu, C.M.; Chiu, C.S.; Chang, H.C.
  • An analysis of how nonresponse error is assessed in academic marketing research
    Collier, J.E.; Bienstock, C.C.
  • Application of the task-technology fit model to structure and evaluate the adoption of e-books by academics
    D’Ambra, J.; Wilson, C.S.; Akter, S.
  • Perceived usefulness, perceived ease of use, and user acceptance of information technology
    Davis, F.D.
  • The DeLone and McLean model of information systems success: a ten-year update
    Delone, W.H.; McLean, E.R.
  • Student satisfaction with online learning: is it a psychological contract?
    Dziuban, C.; Moskal, P.; Thompson, J.; Kramer, L.; DeCantis, G.; Hermsdorfer, A.
  • Understanding knowledge management system antecedents of performance impact: extending the task-technology fit model with intention to share knowledge construct
    El Said, G.R.
  • Relationship between student profile, tool use, participation, and academic performance with the use of augmented reality technology for visualized architecture models
    Fonseca, D.; Martí, N.; Redondo, E.; Navarro, I.; Sánchez, A.
  • Evaluating structural equation models with unobservable and measurement error
    Fornell, C.; Larcker, D.
  • Links: what is an intelligent tutoring system?
    Freedman, R.; Ali, S.S.; McRoy, S.
  • Task-technology fit for mobile information systems
    Gebauer, J.; Shaw, M.J.; Gribbins, M.L.
  • Searching for new answers: the application of task-technology fit to e-textbook usage
    Gerhart, N.; Peak, D.A.; Prybutok, V.R.
  • Modelling smartphone addiction: the role of smartphone usage, self-regulation, general self-efficacy and cyberloafing in university students
    Gökçearslan, S.; Mumcu, F.K.; Haşlaman, T.; Çevik, Y.D.
  • Development and measurement validity of a task-technology fit instrument for user evaluations of information system
    Goodhue, D.L.
  • Task-technology fit and individual performance
    Goodhue, D.L.; Thompson, R.L.
  • The impact of IT outsourcing on information systems success
    Gorla, N.; Somers, T.M.
  • Organizational impact of system quality, information quality, and service quality
    Gorla, N.; Somers, T.M.; Wong, B.
  • An index and test of linear moderated mediation
    Hayes, A.F.
  • Reducing the service deficit in m-commerce: how service-technology fit can support digital sales of complex products
    Heinze, J.; Matt, C.
  • Refining and extending task–technology fit theory: creation of two task–technology fit scales and empirical clarification of the construct
    Howard, M.C.; Rose, J.C.
  • Effects of intelligent tutoring systems in basic algebra courses on subsequent mathematics lecture courses (Order No. 3618883)
    Hrubik-Vulanovic, T.
  • Effects of different online peer-feedback approaches on students’ performance skills, motivation and self-efficacy in a dance course
    Hsia, L.H.; Huang, I.; Hwang, G.J.
  • Intelligent tutoring systems work as a math gap reducer in 6th grade after-school program
    Huang, X.; Craig, S.D.; Xie, J.; Graesser, A.; Hu, X.
  • Teachers’ engagement and students’ satisfaction with a playful learning environment
    Kangas, M.; Siklander, P.; Randolph, J.; Ruokamo, H.
  • Constructs of student-centered online learning on learning satisfaction of a diverse online student body: a structural equation modeling approach
    Ke, F.; Kwak, D.
  • Service quality and student/customer satisfaction in the private tertiary education sector in Singapore
    Khoo, S.; Ha, H.; McGregor, S.L.
  • Motivations and use context in mobile tourism shopping: applying contingency and task–technology fit theories
    Kim, M.J.; Chung, N.; Lee, C.K.; Preis, M.W.
  • 7 things you should know about intelligent tutoring systems
    Koedinger, K.; Tanner, M.
  • The role of task-technology fit as users’ motivation to continue information system use
    Larsen, T.J.; Sørebø, A.M.; Sørebø, Ø.
  • Oscar: an intelligent conversational agent tutor to estimate learning styles
    Latham, A.M.; Crockett, K.A.; McLean, D.A.; Edmonds, B.; O’Shea, K.
  • Surveying instructor and learner attitudes toward e-learning
    Liaw, S.S.; Huang, H.M.; Chen, G.D.
  • A task–technology fit view of learning management system impact
    McGill, T.J.; Klobas, J.E.
  • Common method bias in marketing: causes, mechanisms, and procedural remedies
    MacKenzie, S.B.; Podsakoff, P.M.
  • E-service quality as antecedent to e-satisfaction: the moderating effect of culture
    Ma̳ Sabiote, C.; Ma Frias, D.; Castañeda, J.A.
  • A review of emotion regulation in intelligent tutoring systems
    Malekzadeh, M.; Mustafa, M.B.; Lahsasna, A.
  • Task-technology fit aware expectation-confirmation model towards understanding of MOOCs continued usage intention
    Ouyang, Y.; Tang, C.; Rong, W.; Zhang, L.; Yin, C.; Xiong, Z.
  • Multi-dimensional students’ evaluation of e-learning systems in the higher education context: an empirical investigation
    Ozkan, S.; Koseler, R.
  • A meta-analytic assessment of the DeLone and McLean IS success model: an examination of IS success at the individual level
    Petter, S.; McLean, E.R.
  • Technology use, self-directed learning, student engagement and academic performance: examining the interrelations
    Rashid, T.; Asghar, H.M.
  • Self-regulated learning: examining the baccalaureate millennial nursing student’s approach
    Robb, M.K.
  • Perceived fit with an academic environment: attitudinal and behavioral outcomes
    Schmitt, N.; Oswald, F.L.; Friede, A.; Imus, A.; Merritt, S.
  • Attribution theory: a theoretical framework for understanding information systems success
    Snead, K.C.; Magal, S.R.; Christensen, L.F.; Ndede-Amadi, A.A.
  • Testing the technology-to-performance chain model
    Staples, D.S.; Seddon, P.
  • A meta-analysis of the effectiveness of intelligent tutoring systems on college students’ academic learning
    Steenbergen-Hu, S.; Cooper, H.
  • Assessing the success of e-government systems: an employee perspective
    Stefanovic, D.; Marjanovic, U.; Delić, M.; Culibrk, D.; Lalic, B.
  • The effects of mobile-computer-supported collaborative learning: meta-analysis and critical synthesis
    Sung, Y.T.; Yang, J.M.; Lee, H.Y.
  • Glossary
  • Online teaching and learning processes: declarative and procedural knowledge
    Torras, E.; Bellot, A.
  • An empirical investigation of employee portal success
    Urbach, N.; Smolnik, S.; Riempp, G.
  • The acceptance and use of a virtual learning environment in China
    Van Raaij, E.M.; Schepers, J.J.
  • Knowledge quality: a review and a revised conceptual model
    Waheed, M.; Kaur, K.
  • The stickiness intention of group-buying websites: the integration of the commitment–trust theory and e-commerce success model
    Wang, W.; Wang, Y.; Liu, E.
  • Assessing e-Government systems success: a validation of the DeLone and McLean model of information systems success
    Wang, Y.S.; Liao, Y.W.
  • Measuring e-learning systems success in an organizational context: scale development and validation
    Wang, Y.S.; Wang, H.Y.; Shee, D.Y.
  • A model for assessing blog-based learningsystems success
    Wang, Y.S; Li, H.; Li, C.; Wang, C.
  • Understanding user perceptions of information systems success
    Whyte, G.; Bytheway, A.; Edwards, C.
  • Continuance intention to use MOOCs: integrating the technology acceptance model (TAM) and task technology fit (TTF) model
    Wu, B.; Chen, X.
  • E-learning success model for instructors’ satisfactions in perspective of interaction and usability outcomes
    Yengin, I.; Karahoca, A.; Karahoca, D.
  • Modelling the factors that affect individuals utilisation of online learning systems: an empirical study combining the task technology fit model with the theory of planned behaviour
    Yu, T.K.; Yu, T.Y.
  • Exploring the effect of social media information quality, source credibility and reputation on informational fit-to-task: moderating role of focused immersion
    Zha, X.; Yang, H.; Yan, Y.; Liu, K.; Huang, C.
  • Laptop programs for students
    Zucker, A.A.; Light, D.
  • Information systems success: the quest for the dependent variable
    DeLone, W.H.; McLean, E.R.