nach oben

Education and Information Technologies

Erschienen in:

07.09.2020

Systematic literature review of STEM self-study related ITSs

verfasst von: Asmaa Alabdulhadi, Maha Faisal

Erschienen in: Education and Information Technologies | Ausgabe 2/2021

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

A simulator-based Intelligent Tutoring System (ITS) is a computer system that is made to provide students with a learning experience that is both customizable to a student’s needs (e.g., level of expertise, pace) and includes simulation, e.g., demonstrate certain domain concepts or allow problem-solving while replicating a real-life situation. ITSs offers convenient and low-cost studying. We aim to explore the recent trends and identify limitations and opportunities in recent work on STEM (Science, Technology, Engineering, and Mathematics) self-study simulator based ITSs, we conducted a systematic literature review investigating 47 papers from four different databases. The research encloses ITSs from various educational sectors, ranging from elementary, middle, secondary, tertiary, and after school training. The majority of the systems targeted tertiary education. As a result, there are many research opportunities in introducing a more generalizable approach to simulator-based ITSs which will make it easier to address many STEM-related subjects. There are also opportunities in utilizing help methods that emphasize encouragement and self-reflection. We noticed that the number of STEM-related simulator-based ITSs is relatively low. Another finding was that most simulator-based ITSs are domain-dependent, and therefore they are not reusable for other subjects. Finally, we found that the traits of feedback in simulator-based systems that result in positive learning outcomes are ones that combined immediate and delayed feedback, used procedural information in their feedback, of formative feedback type, and detailed feedback.

Vorheriger Artikel A machine learning approximation of the 2015 Portuguese high school student grades: A hybrid approach

Nächster Artikel Toward a model for acceptance of MOOCs in higher education: the modified UTAUT model for Saudi Arabia

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Nur mit Berechtigung zugänglich

Alepis, E., & Virvou, M. (2011). Automatic generation of emotions in tutoring agents for affective e-learning in medical education. Expert Systems with Applications, 38(8), 9840–9847. https://doi.org/10.1016/j.eswa.2011.02.021.CrossRef

Arnau, D., Arevalillo-Herráez, M., Puig, L., & González-Calero, J. A. (2013). Fundamentals of the design and the operation of an intelligent tutoring system for the learning of the arithmetical and algebraic way of solving word problems. Computers and Education, 63, 119–130. https://doi.org/10.1016/j.compedu.2012.11.020.CrossRef

Bimba, A. T., Idris, N., Al-Hunaiyyan, A., Mahmud, R. B., & Shuib, N. L. B. M. (2017). Adaptive feedback in computer-based learning environments: A review. Adaptive Behavior, 25(5), 217–234. https://doi.org/10.1177/1059712317727590.CrossRef

Boulay, B. (2016). Artificial intelligence as an effective classroom assistant, 16–21.

Bringula, R. P., Basa, R. S., Dela Cruz, C., & Rodrigo, M. M. T. (2015). Effects of prior knowledge in mathematics on learner-interface interactions in a learning-by-teaching intelligent tutoring system. Journal of Educational Computing Research, 54(4), 462–482. https://doi.org/10.1177/0735633115622213.CrossRef

Bybee, R. W. (2013). The case for STEM education: Challenges and opportunities. Arlington: NSTA press.

Castro-Schez, J. J., Gallardo, J., Miguel, R., & Vallejo, D. (2017). Knowledge-based systems to enhance learning: A case study on formal languages and automata theory. Knowledge-Based Systems, 122, 180–198. https://doi.org/10.1016/j.knosys.2017.02.007.CrossRef

Chou, C., Huang, B., & Lin, C. (2011). Complementary machine intelligence and human intelligence in virtual teaching assistant for tutoring program tracing. Computers & Education, 57(4), 2303–2312. https://doi.org/10.1016/j.compedu.2011.06.005.CrossRef

Chrysafiadi, K., & Virvou, M. (2015). Fuzzy logic for adaptive instruction in an e-learning environment for computer programming. IEEE Transactions on Fuzzy Systems, 23(1), 164–177. https://doi.org/10.1109/TFUZZ.2014.2310242.CrossRef

Corbett, A., Kauffman, L., Maclaren, B., Wagner, A., & Jones, E. (2010). A cognitive tutor for genetics problem solving: Learning gains and student modeling. Journal of Educational Computing Research, 42(2), 219–239. https://doi.org/10.2190/EC.42.2.e.CrossRef

Crockett, K., Latham, A., & Whitton, N. (2017). On predicting learning styles in conversational intelligent tutoring systems using fuzzy decision trees. Journal of Human Computer Studies, 97, 98–115. https://doi.org/10.1016/j.ijhcs.2016.08.005.

Cunha-pérez, C., Arevalillo-herráez, M., Marco-giménez, L., & Arnau, D. (2018). On incorporating affective support to an intelligent tutoring system. An empirical study, 13(2), 63–69.

Dahotre, A., Krishnamoorthy, V., Corley, M., Scaffidi, C. (2011). Using intelligent tutors to enhance student learning of application programming interfaces*, 195–201.

Ding, Q., & Cao, S. (2017). RECT: A cloud-based learning tool for graduate software engineering practice courses with remote tutor support, 5.

Dolenc, K., & Aber, B. (2015). TECH8 intelligent and adaptive e-learning system: Integration into technology and science classrooms in lower secondary schools, 82, 354–365. https://doi.org/10.1016/j.compedu.2014.12.010

De Bra, P. (2017). Challenges in user modeling and personalization. IEEE Intelligent Systems, 32(5), 76–80. https://doi.org/10.1109/MIS.2017.3711638.CrossRef

Fournier-Viger, P., Nkambou, R., Nguifo, E. M., Mayers, A., & Faghihi, U. (2013). A multiparadigm intelligent tutoring system for robotic arm training. IEEE Transactions on Learning Technologies, 6(4), 364–377. https://doi.org/10.1109/TLT.2013.27.

Gálvez, J., Guzmán, E., Conejo, R., Mitrovic, A., Mathews, M. (2016). Data calibration for statistical-based assessment in constraint-based tutors. 97, 11–23. https://doi.org/10.1016/j.knosys.2016.01.024.

González, J. A., Jover, L., Cobo, E., Muñoz, P. (2010). A web-based learning tool improves student performance in statistics: A randomized masked trial, 55, 704–713. https://doi.org/10.1016/j.compedu.2010.03.003.

Greer, J., & Mark, M. (2016). Evaluation methods for intelligent tutoring systems revisited. International Journal of Artificial Intelligence in Education, 26(1), 387–392. https://doi.org/10.1007/s40593-015-0043-2.CrossRef

Hatzilygeroudis, I., & Prentzas, J. (2004). Knowledge representation requirements for intelligent tutoring systems. In International Conference on Intelligent Tutoring Systems (pp. 87–97). Berlin: Springer.CrossRef

Hooshyar, D., Binti Ahmad, R., Wang, M., Yousefi, M., Fathi, M., & Lim, H. (2017). Development and evaluation of a game-based bayesian intelligent tutoring system for teaching programming. Journal of Educational Computing Research, 56(6), 775–801. https://doi.org/10.1177/0735633117731872.CrossRef

Hooshyar, D., Binti, R., Youse, M., Fathi, M., Horng, S., Lim, H. (2016). Applying an online game-based formative assessment in a flowchart-based intelligent tutoring system for improving problem-solving skills. 94, 36. https://doi.org/10.1016/j.compedu.2015.10.013.

Iqbal, A., Oppermann, R., Patel, A. (1999). A classification of evaluation methods for intelligent tutoring systems.

Jang, E. E., Lajoie, S. P., Wagner, M., Xu, Z., Poitras, E. (2017). Person-oriented approaches to profiling learners in technology-rich learning environments for ecological learner modeling. https://doi.org/10.1177/0735633116678995.

Jaques, P. A., Seffrin, H., Rubi, G., De Morais, F., Ghilardi, C., Bittencourt, I. I., & Isotani, S. (2013). Rule-based expert systems to support step-by-step guidance in algebraic problem solving: The case of the tutor PAT2Math. Expert Systems with Applications, 40(14), 5456–5465. https://doi.org/10.1016/j.eswa.2013.04.004.CrossRef

Jeremić, Z., Jovanović, J., & Gašević, D. (2012). Student modeling and assessment in intelligent tutoring of software patterns. Expert Systems with Applications, 39(1), 210–222. https://doi.org/10.1016/j.eswa.2011.07.010.

Jovanovic, D., & Jovanovic, S. (2015). An adaptive e-learning system for Java programming course, based on Dokeos le. Computer Applications in Engineering Education, 23(3), 337–343. https://doi.org/10.1002/cae.21603.CrossRef

Karakostas, B. (2013). A model and content-driven e-learning platform. 2013 2nd international conference on E-learning and E-Technologies in Education, ICEEE 2013, 127–130. https://doi.org/10.1109/ICeLeTE.2013.6644360.

Klašnja-Milićević, A., Vesin, B., Ivanović, M., & Budimac, Z. (2011). E-learning personalization based on hybrid recommendation strategy and learning style identification. Computers and Education, 56(3), 885–899. https://doi.org/10.1016/j.compedu.2010.11.001.CrossRef

Koedinger, K. R., McLaughlin, E. A., & Heffernan, N. T. (2010). A quasi-experimental evaluation of an on-line formative assessment and tutoring system. Journal of Educational Computing Research, 43(4), 489–510. https://doi.org/10.2190/EC.43.4.d.CrossRef

Kopp, K. J., Britt, M. A., Millis, K., & Graesser, A. C. (2012). Improving the efficiency of dialogue in tutoring. Learning and Instruction, 22, 320–330. https://doi.org/10.1016/j.learninstruc.2011.12.002.CrossRef

Latham, A., Crockett, K., Mclean, D., & Edmonds, B. (2012). A conversational intelligent tutoring system to automatically predict learning styles. Computers & Education, 59(1), 95–109. https://doi.org/10.1016/j.compedu.2011.11.001.CrossRef

Lazar, T., Sadikov, A., & Bratko, I. (2017). Rewrite rules for debugging student programs in programming tutors. IEEE Transactions on Learning Technologies, 11(4), 429–440. https://doi.org/10.1109/TLT.2017.2743701.CrossRef

Lee, Y. (2010). Effects of instructional preparation strategies on problem solving in a web-based learning environment*. 42(4), 385–406. https://doi.org/10.2190/EC.42.4.b.

Lee, Y.-J. (2011). Utilizing formative assessments to guide student learning in an interactive physics learning environment. Journal of Educational Technology Systems, 39(3), 245–260. https://doi.org/10.2190/ET.39.3.c.CrossRef

Mark, M. A., & Greer, J. E. (1993). Evaluation methodologies for intelligent tutoring systems. Journal of Artificial Intelligence in Education, 4, 129–129.

Martens, A. (2008). Simulation in teaching and training. Encyclopedia of Information Technology Curriculum Integration, 764–770.

Martens, A., & Himmelspach, J. (2005). Combining intelligent tutoring and simulation systems. Proc. of the Internat. Conference on Simulation in Human Computer Interfaces, 5, 65–70.

Mclaren, B. M., Deleeuw, K. E., & Mayer, R. E. (2011). Polite web-based intelligent tutors : Can they improve learning in classrooms ? Computers & Education, 56(3), 574–584. https://doi.org/10.1016/j.compedu.2010.09.019.CrossRef

Mitrovic, A., Ohlsson, S., & Barrow, D. K. (2013). The effect of positive feedback in a constraint-based intelligent tutoring system. Computers & Education, 60(1), 264–272. https://doi.org/10.1016/j.compedu.2012.07.002.CrossRef

Monterrat, B., & Lavoué, É. (2017). Adaptation of gaming features for motivating learners. https://doi.org/10.1177/1046878117712632.

Myneni, L. S., Narayanan, N. H., Rebello, S., Rouinfar, A., & Pumtambekar, S. (2013). An interactive and intelligent learning system for physics education. IEEE Transactions on Learning Technologies, 6(3), 228–239.CrossRef

Narciss, S., Sosnovsky, S., Schnaubert, L., Andrès, E., Eichelmann, A., Goguadze, G., & Melis, E. (2014). Exploring feedback and student characteristics relevant for personalizing feedback strategies. Computers & Education, 71, 56–76. https://doi.org/10.1016/j.compedu.2013.09.011.CrossRef

Özyurt, Ö., Özyurt, H., & Baki, A. (2013). Design and development of an innovative individualized adaptive and intelligent e-learning system for teaching–learning of probability unit: Details of UZWEBMAT. Expert Systems with Applications, 40(8), 2914–2940. https://doi.org/10.1016/j.eswa.2012.12.008.CrossRef

Paiva, R. C., Ferreira, M. S., Mendes, A. G., Euse, A. M. J. (2015). Interactive and multimedia contents associated with a system for computer-aided assessment. https://doi.org/10.1177/0735633115571305.

Paravati, G., Member, S., Lamberti, F., Member, S. (2017). Point cloud-based automatic assessment of 3D computer animation courseworks. 10(4), 532–543.

Rajendran, R., Iyer, S., & Murthy, S. (2018). Personalized affective feedback to address students’ frustration in ITS. IEEE Transactions on Learning Technologies, 12(1), 87–97. https://doi.org/10.1109/TLT.2018.2807447.CrossRef

Ramírez, J., Rico, M., Riofrío-Luzcando, D., Berrocal-Lobo, M., & de Antonio, A. (2018). Students’ evaluation of a virtual world for procedural training in a tertiary-education course. Journal of Educational Computing Research, 56(1), 23–47. https://doi.org/10.1177/0735633117706047.CrossRef

Rau, M. A., Michaelis, J. E., & Fay, N. (2015). Connection making between multiple graphical representations: A multi-methods approach for domain-specific grounding of an intelligent tutoring system for chemistry. Computers and Education, 82, 460–485. https://doi.org/10.1016/j.compedu.2014.12.009.CrossRef

Riofrío-Luzcando, D., Ramirez, J., & Berrocal-Lobo, M. (2017). Predicting student actions in a procedural training environment. IEEE Transactions on Learning Technologies, 10(4), 463–474.CrossRef

Rodrigo, M. M. T., Baker, R. S. J. D., Agapito, J., Nabos, J., Concepcion Repalam, M., Reyes Jr., S. S., & San Pedro, M. O. C. Z. (2012). The effects of an interactive software agent on student affective dynamics while using an intelligent tutoring system. IEEE Transactions on Affective Computing, 3(2), 224–236. https://doi.org/10.1109/T-AFFC.2011.41.CrossRef

Roll, I., Aleven, V., McLaren, B. M., & Koedinger, K. R. (2011). Improving students’ help-seeking skills using metacognitive feedback in an intelligent tutoring system. Learning and Instruction, 21(2), 267–280. https://doi.org/10.1016/j.learninstruc.2010.07.004.CrossRef

Sehrawat, A., Keelan, R., Shimada, K., Wilfong, D. M., McCormick, J. T., & Rabin, Y. (2016). Simulation-based cryosurgery intelligent tutoring system prototype. Technology in Cancer Research and Treatment, 15(2), 396–407. https://doi.org/10.1177/1533034615583187.CrossRef

Van Seters, J. R., Ossevoort, M. A., Tramper, J., & Goedhart, M. J. (2012). The influence of student characteristics on the use of adaptive e-learning material. Computers & Education, 58(3), 942–952. https://doi.org/10.1016/j.compedu.2011.11.002.CrossRef

Trenas, M. A., Ramos, J., Gutierrez, E. D., Romero, S., & Corbera, F. (2011). Use of a new moodle module for improving the teaching of a basic course on computer architecture. IEEE Transactions on Education, 54(2), 222–228.CrossRef

Vaessen, B. E., Prins, F. J., & Jeuring, J. (2014). University students ’ achievement goals and help-seeking strategies in an intelligent tutoring system. Computers & Education, 72, 196–208. https://doi.org/10.1016/j.compedu.2013.11.001.CrossRef

VanLehn, K., Wetzel, J., Grover, S., & Van De Sande, B. (2017). Learning how to construct models of dynamic systems: An initial evaluation of the dragoon intelligent tutoring system. IEEE Transactions on Learning Technologies, 10(2), 154–167.CrossRef

Wang, D., Han, H., Zhan, Z., Xu, J., Liu, Q., & Ren, G. (2015). A problem solving oriented intelligent tutoring system to improve students’ acquisition of basic computer skills. Computers and Education, 81, 102–112. https://doi.org/10.1016/j.compedu.2014.10.003.CrossRef

Zhang, L., Vanlehn, K., Girard, S., Burleson, W., Chavez-echeagaray, M. E., Gonzalez-sanchez, J., & Hidalgo-pontet, Y. (2014). Evaluation of a meta-tutor for constructing models of dynamic systems. Computers & Education, 75, 196–217. https://doi.org/10.1016/j.compedu.2014.02.015.CrossRef

Titel: Systematic literature review of STEM self-study related ITSs
verfasst von: Asmaa Alabdulhadi
Maha Faisal
Publikationsdatum: 07.09.2020
Verlag: Springer US
Erschienen in: Education and Information Technologies / Ausgabe 2/2021
Print ISSN: 1360-2357
Elektronische ISSN: 1573-7608
DOI: https://doi.org/10.1007/s10639-020-10315-z

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 2/2021

Playfulness in the classroom: Gamification favor the learning of pharmacology

User acceptance of learning innovation: A structural equation modelling based on the GUAM framework

Facilitating the development of Preservice teachers’ proportional reasoning in geometric similarity problems using augmented reality activities

Question generation model based on key-phrase, context-free grammar, and Bloom’s taxonomy

Cloud feasibility and adoption strategy for the INDIAN school education system

Incorporating new literacies in designing a mobile learning application for secondary/middle school students