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Oussama, H. Artificial Intelligent in Education. Encyclopedia. Available online: https://encyclopedia.pub/entry/20101 (accessed on 23 April 2024).
Oussama H. Artificial Intelligent in Education. Encyclopedia. Available at: https://encyclopedia.pub/entry/20101. Accessed April 23, 2024.
Oussama, Hamal. "Artificial Intelligent in Education" Encyclopedia, https://encyclopedia.pub/entry/20101 (accessed April 23, 2024).
Oussama, H. (2022, March 02). Artificial Intelligent in Education. In Encyclopedia. https://encyclopedia.pub/entry/20101
Oussama, Hamal. "Artificial Intelligent in Education." Encyclopedia. Web. 02 March, 2022.
Artificial Intelligent in Education
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The application of Artificial Intelligence or AI in education has been the subject of academic research. The field examines learning wherever it occurs, in traditional classrooms or at workplaces so to support formal education and lifelong learning. It combines interdisciplinary AI and learning sciences (such as education, psychology, neuroscience, linguistics, sociology and anthropology) in order to facilitate the development of effective adaptive learning environments and various flexible, inclusive tools. Nowadays, there are several new challenges in the field of education technology in the era of smart phones, tablets, cloud computing, Big Data, etc., whose current research questions focus on concepts such as ICT-enabled personalized learning, mobile learning, educational games, collaborative learning on social media, MOOCs, augmented reality application in education and so on. Therefore, to meet these new challenges in education, several fields of research using AI have emerged over time to improve teaching and learning using digital technologies.

personalized learning mobile learning educational collaborative learning on social media MOOCs AIED EDM LA

1. Introduction

The application of AI in education has been the subject of academic research for more than 30 years. The field examines learning wherever it occurs, in traditional classrooms or at workplaces so to support formal education and lifelong learning. It combines interdisciplinary AI and learning sciences (such as education, psychology, neuroscience, linguistics, sociology and anthropology) in order to facilitate the development of effective adaptive learning environments and various flexible, inclusive tools. It is personalized and attractive for teaching and learning [1]. The same goes with the AIED, which focuses on issues related to the theories of human learning and AI application in effective learning environments, as well as theories of teaching and AI application to effective educational systems. It is clear that in many cases, there is a fuzzy boundary between learning environments and teaching systems [2].

2. Artificial Intelligence in Education (AIED)

2.1. The Objectives of the AIED

The scientific goal of AIED is to define specific and explicit forms of knowledge about education, including several psychological and social aspects which often remain implicit [3]. In addition to driving many “smart” technologies, AIED is intended as a powerful way to explore in detail what is sometimes called the “black box of learning.” This helps to better understand how learning actually occurs; to analyze the influence of the socio-economic factors of the learner [4], the physical context and the technology [5] or to study the nature of knowledge and its representation. Determining the most appropriate way to learn and the most effective teaching interaction styles helps a learner in his/her learning, especially when it is used in the right moments. To identifying the misconceptions that learners have about the learning object, AIED effectively involves two complementary components: developing AI-based tools to support learning and using these tools to help understand learning. For example, by modeling how learners solve an arithmetic problem and by identifying misconceptions previously unknown to educators, researchers and teachers can learn much more about the learning process itself, and these understandings could then be applied to classical classroom practices [6]. In addition, they could be integrated into the development of AIED tools [1].

2.2. The Strategy of the AIED

Researchers in AIED are paying increasing attention to the emotional [7], social [3] and intellectual aspects of learning with very active research conducted in the study of collaboration [8], metacognition [9], self-regulation, motivation and emotions [10]. This research is motivated by educational problems and focuses as much on research as on technological development. Research based on theory is supported by a systematic empirical evaluation that informs the further development of the theory. The AIED community is actively exploring ways in which learning and education can take advantages of new and advanced technologies, including advances in AI [11].

2.3. Example of AIED Tools: Intelligent Tutoring Systems

Intelligent tutoring systems or ITS is one of the most common applications of AI in education, or at least it is probably the oldest. An ITS generally offers step-by-step tutorials on topics in well-defined and structured subjects such as mathematics or physics, which are customized for each learner. That is, it relies on specialized knowledge about the subject and a pedagogical approach. In response to the misconceptions and correctness of each learner, the system determines step by step an optimal path through the support and learning activities. As the learner progresses, the system automatically adjusts the level of difficulty and provides hints or tips that all aim to ensure that the learner is able to learn the given subject effectively [12]. Some ITSs allow learners to control their own learning to help them develop self-regulatory skills; others use instructional strategies to regulate the progression of learning to support the learner [1]. ITSs are based on models that represent knowledge specific to teaching and learning. In general, there are three types of knowledge. Firstly, knowledge about the subject to be learned is represented in what is so-called a domain model. Secondly, knowledge about effective teaching approaches is represented in a pedagogical model. Thirdly, knowledge about the learner is represented in a learner model. From these three models, algorithms can adapt a sequence of learning activities to each learner [13]. Instead of models, many recent ITSs use machine learning techniques, self-learning algorithms based on large data sets and neural networks to enable them to make appropriate content which then is provided to the learner. However, with this approach, it may be difficult to explain the rationale for these decisions [1] (Figure 1).
Figure 1. Simplified Schema of ITS based on a model.

2.3.1. The Domain Model

A domain model represents the knowledge that ITS aims to help learners acquire. This may include, for example, knowledge of mathematical procedures, genetic heritage, or causes of the First World War [10]. In fact, mathematics for elementary and high school students has dominated ITSs over the years. Physics and computer science are also fruits within reach of ITSs because they are, at least at basic levels, well-structured and clearly defined [14].

2.3.2. The Educational Model

The pedagogical model represents knowledge of effective teaching and learning approaches that have been obtained from pedagogical experts and research in the learning sciences [14]. The pedagogical knowledge that has been represented in many ITSs includes knowledge of pedagogical approaches [15], proximal developmental area, interlaced practice, cognitive load and formative return. For example, a teaching model using the Vygotsky Proximal Development Zone ensures that the activities provided by the system to the learner are neither too easy nor too stimulating. A model implementing an individualized formative return ensures that the return is provided to the learner whenever it is possible to give support.

2.3.3. The Model of the Learner

What distinguishes AI-based ITSs is that they also include a learner model; that is, a representation of the learner’s state of knowledge. In fact, many ITSs incorporate a wide range of knowledge about the learner such as their interactions, the material that are challenging to the learner, their misconceptions, and their emotional states when using the system. This information can be used to inform the progress of the learning process and therefore determine the support that will be given to the learner. When most ITSs go much further, the knowledge stored on each learner is supplemented by the knowledge of all learners who have already used the system. From the data of all learners, therefore, the system learns to predict which pedagogical approach and field are appropriate for a particular learner. It is the learner model that allows ITSs to be adaptive, and machine learning makes this adaptive process more efficient [16].

3. Educational Data Mining (EDM)

EDM is an emerging field linked to several established research areas, including e-learning, adaptive hypermedia, intelligent tutoring systems, online exploration, data mining and so on. The application of data mining in education systems has specific requirements that are not present in other areas, mainly the need to take into account the pedagogical aspects of the learner and the system. Although the exploration of educational data is a very recent field of research, a large number of contributions published in journals, international congresses, specific workshops and works in progress [17] show that is a promising new field. EDM is concerned with developing, researching and applying computer-based methods to detect schemas of large educational data collections [18]. These patterns would, otherwise, be difficult or impossible to analyze directly because of the huge amount of data in which they exist. Data of interest is not limited to learner interactions with an educational system (e.g., navigation behavior, questionnaire entry and interactive exercises), but it may also include data from collaborating learners (textual dialogue, for example), administrative data (school, etc.), and demographic data (such as gender, age, school results). Data on learner states (motivation, emotional states, for example) has also been taken into account, which can be deduced from physiological sensors (facial expression, seat posture and perspiration, for example). EDM uses methods and tools from the broader field of data mining [8][19], and a subdomain of computer science and AI that has been used for purposes as diverse as credit card fraud detection, genetic sequence analysis in bioinformatics, or analysis of customer buying behavior [20].

3.1. Attempts to Define EDM

EDM is defined as the area of scientific inquiry focused on the development of methods for discovering types of data uniquely derived from educational contexts, and for using these methods to better understand learners and the context learning [8][15]. In other words, EDM is about converting raw data from educational systems into useful information that can be used to inform design decisions and answer research questions [21].

3.2. The Main Approaches in EDM

Data mining, in general, encompasses a wide range of search techniques that include more traditional options such as database queries and simple automatic logging, as well as more recent developments in machine learning and linguistic technology [21]. EDM methods often differ from methods in the broader literature on data mining, explicitly exploiting the multiple, hierarchical, significant levels of educational data. Psychometric methods are often incorporated into machine learning and data mining methods to achieve this goal [22]. As a result of this, there is a wide variety of common popular methods in educational data mining (Table 1). These methods fall into the following general categories: forecasting, grouping, relationship exploration, discovery with models and data distillation for human judgment. The first three categories are widely recognized as universal for all types of data mining (although they are in some cases under different names). The fourth and fifth categories gain special importance in the exploration of educational data, and so on [22].
Table 1. The main categories of analyzes in EDM.
Category of Method Goals of Method Key Applications
Prediction Develop a model which can infer a single aspect of the data (predicted variable) from some combination of other aspectsof the data (predictor variables) Detecting student behaviors (e.g., gaming the system, off-task behavior, slipping); developing domain models; predicting and understanding student educational outcomes.
Clustering Find data points that naturally group together, slipping the full data set into a set of categories. Discovery of new student behavior patterns; investigating similarities and differences between schools
Relationship mining Discover relationships between variables Discovery of curricular associations in course sequences; Discovering which pedagogical strategies lead to more effective/robust learning
Discovery with models A model of a phenomenon developed with prediction, clustering or knowledge engineering, is used as a component in further prediction or relationship mining Discovery of relationships between student behaviors, and student characteristics or contextual variables; Analysis of research questions across wide variety of contexts.
Distillation of data for Human judgment Data is distilled to enable a human to quickly identify or classify features of the data. Human Identification of patterns in student learning, behavior, or collaboration; Labeling data for use in later development of prediction model
A large number of EDM applications have been used, four areas of application deserve special attention. One of the main areas of application is the improvement of learner models, which is providing detailed information on learner characteristics or states such as knowledge, motivation, meta-cognition and attitude. Modelling individual differences between learners to allow software to address these differences is a key theme in educational software research. A second key application area is to discover or improve models of the domain knowledge structure. In EDM, methods have been created for rapid discovery of specific domain models directly from data. A third key application area is to study the educational support provided by learning software. Modern educational software offers various types of educational support to learners. Finding the most effective educational support is a key area of interest in EDM. The decomposition of learning, a type of relationship exploration, adapts exponential learning curves to performance data so to link learner success to the quantity of each type of instructional medium that a learner has received (with a weight for each type of support) [23]. The weights indicate the effectiveness of each type of pedagogical support to improve learning. A fourth key application area of the EDM is scientific discovery about learning and learners. It therefore takes many forms. The EDM to answer questions in one of the three areas previously discussed (such as learner models, domain models and pedagogical support) may have broader scientific benefits. For example, the study of pedagogical support may have the long-term potential to enrich the theories of learning [24].

3.3. Examples of Applications of EDM

A typical feature of educational data is its non-independence. That is, when researchers collect data from educational discussions and when researchers want to rank whether the contributions are on a topic or not, researchers must consider that the contributions are not statistically independent of each other because many contributions may come from the same learner or discussion. This could be detrimental to the calculation of models (standard machine learning schemes usually include the built-in assumption of independent training examples) as well as model validation (for example, cross-validation could lead to biased results when the training and the set of tests are not independent).
In addition, the results of research in EDM are generally obtained in the narrow context of specific research projects and educational contexts (such as a particular school). The question is how general these results are, for instance, if the same learner model parameters can also be used with other learner populations or if a predictive model is always reliable in a different context. Therefore, there is a growing need for replication studies to test broader generalizations. As a result, EDM researchers are increasingly interested in open data repositories and standard data formats to promote the exchange of data and models [18].
EDM is a young field of research, and it is necessary to initiate more specialized and oriented professional training in order to achieve a level of success similar to that of other fields such as the extraction of medical data, extraction of e-commerce data, and so on [25].

References

  1. Pai, K.C.; Kuo, B.C.; Liao, C.H.; Liu, Y.M. An application of Chinese dialogue-based intelligent tutoring system in remedial instruction for mathematics learning. Educ. Psychol. 2021, 41, 137–152.
  2. Du Boulay, B.; Lajoie, S.P. Fallible, distractible, forgetful, willful and irrational learners. In Computers as Cognitive Tools, Volume Two: No More Walls; Routledge: New York, NY, USA, 2000; pp. 339–376.
  3. Baker, R.S.; Inventado, P.S. Educational data mining and learning analytics. In Learning Analytics; Springer: New York, NY, USA, 2014; pp. 61–75.
  4. Suthers, D.; Verbert, K. Learning analytics as a middle space. In Proceedings of the Third International Conference on Learning Analytics and Knowledge, Leuven, Belgium, 8–13 April 2013; ACM: New York, NY, USA, 2013; pp. 1–4.
  5. Chiu, C.K.; Tseng, J.C. A bayesian classification network-based learning status management system in an intelligent classroom. Educ. Technol. Soc. 2021, 24, 256–267.
  6. Holmes, W.; Bialik, M.; Fadel, C. Artificial Intelligence in Education: Promises and Implications for Teaching and Learning; Center for Curriculum Redesign: Boston, MA, USA, 2019; ISBN 978-1-79429-370-0.
  7. Underwood, J.; Luckin, R. What Is AIED and Why Does Education Need It. A report for the UK’s TLRP Technology Enhanced Learning—Artificial Intelligence in Education Theme. May 2011. Available online: https://www.researchgate.net/publication/41698223_What_is_AIED_and_why_does_%20Education_need_it (accessed on 10 December 2021).
  8. Zhang, Y.; Paquette, L.; Baker, R.S.; Ocumpaugh, J.; Bosch, N.; Biswas, G.; Munshi, A. Can Strategic Behaviour Facilitate Confusion Resolution? The Interplay Between Confusion and Metacognitive Strategies in Betty’s Brain. J. Learn. Anal. 2021, 8, 1–17.
  9. Hamal, O.; El Faddouli, N.E.; Harouni, M.H.A. Design and implementation of the multi-agent system in education. World J. Educ. Technol. Curr. Issues 2021, 13, 775–793.
  10. Utterberg Modén, M.; Tallvid, M.; Lundin, J.; Lindström, B. Intelligent Tutoring Systems: Why Teachers Abandoned a Tech-nology Aimed at Automating Teaching Processes. In Proceedings of the 54th Hawaii International Conference on System Sciences, Kauai, HI, USA, 5–8 January 2021; p. 1538.
  11. Siemens, G.; Baker, R.S. Learning analytics and educational data mining: Towards communication and collaboration. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, Vancouver, BC, Canada, 29 April–2 May 2012; ACM: New York, NY, USA, 2012; pp. 252–254.
  12. Romero, C.; Ventura, S. Educational data mining: A survey from 1995 to 2005. Expert Syst. Appl. 2007, 33, 135–146.
  13. Castro-Schez, J.J.; Glez-Morcillo, C.; Albusac, J.; Vallejo, D. An intelligent tutoring system for supporting active learning: A case study on predictive parsing learning. Inf. Sci. 2021, 544, 446–468.
  14. Siemens, G. Learning analytics: Envisioning a research discipline and a domain of practice. In Proceedings of the 2nd Inter-national Conference on Learning Analytics and Knowledge, Vancouver, BC, Canada, 29 April–2 May 2012; ACM: New York, NY, USA, 2012; pp. 4–8.
  15. Witten, I.H.; Frank, E. Data Mining: Practical Machine Learning Tools and Techniques, 2nd ed.; Morgan Kaufmann: San Francisco, CA, USA, 2005.
  16. Heiner, C.; Heffernan, N.; Barnes, T. Educational data mining. In Proceedings of the 13th International Conference of Artificial Intelligence in Education, Marina del Rey, CA, USA, 23 July 2007.
  17. Baker, R. Data mining for education. Int. Encycl. Educ. 2010, 7, 112–118.
  18. Beck, J.E.; Mostow, J. How who should practice: Using learning decomposition to evaluate the efficacy of different types of practice for different types of students. In Proceedings of the 9th International Conference on Intelligent Tutoring Systems, Montreal, QC, Canada, 23–27 June 2008; Springer: Berlin/Heidelberg, Germany, 2008; pp. 353–362.
  19. Schiff, D. Education for AI, not AI for Education: The Role of Education and Ethics in National AI Policy Strategies. Int. J. Artif. Intell. Educ. 2021, 1–37.
  20. Rohal, M.; Barrera, N.; Escobar-Briones, E.; Brooks, G.; Hollander, D.; Larson, R.; Montagna, P.A.; Pryor, M.; Romero, I.C.; Schwing, P. How quickly will the offshore ecosystem recover from the 2010 Deepwater Horizon oil spill? Lessons learned from the 1979 Ixtoc-1 oil well blowout. Ecol. Indic. 2020, 117, 106593.
  21. Thomas, C.C.; Otis, N.G.; Abraham, J.R.; Markus, H.R.; Walton, G.M. Toward a science of delivering aid with dignity: Experimental evidence and local forecasts from Kenya. Proc. Natl. Acad. Sci. USA 2020, 117, 15546–15553.
  22. Elias, T. Learning Analytics: Definitions, Processes, and Potential. Creative Commons. Available online: http://learninganalytics.net/LearningAnalyticsDefinitionsProcessesPotential.pdf (accessed on 18 October 2021).
  23. Ferguson, R. Learning analytics: Drivers, developments and challenges. Int. J. Technol. Enhanc. Learn. 2012, 4, 304–317.
  24. Liñán, L.C.; Pérez, A.J. Educational Data Mining and Learning Analytics: Differences, similarities, and time evolution. Int. J. Educ. Technol. High. Educ. 2015, 12, 98–112.
  25. Surubaru, N.C. European funds in Central and Eastern Europe: Drivers of change or mere funding transfers? Evaluating the impact of European aid on national and local development in Bulgaria and Romania. Eur. Politics Soc. 2021, 22, 203–221.
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