View of ROLE OF ARTIFICIAL INTELLIGENCE AND BIG DATA IN EDUCATION: A SUGGESTIVE STUDY

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Vol.04,Special Issue 04, ² Conference (ICIRSTM) April 2019, Available Online: www.ajeee.co.in/index.php/AJEEE

ROLE OF ARTIFICIAL INTELLIGENCE AND BIG DATA IN EDUCATION: A SUGGESTIVE STUDY

Aakriti Singla

Student, SET, Mody University, Lakshmangarh, Sikar, Rajasthan Nikita Bajaj

Student, SET, Mody University, Lakshmangarh, Sikar, Rajasthan Dr Sanjeev Patwa

SET, Mody University, Lakshmangarh, Sikar, Rajasthan

Abstract - The educational sphere is abuzz with three upcoming technological advancements including Artificial Intelligence, Machine Learning and Big Data. While everyone opines that the current educational model fails miserably in making learning relevant to knowledge-seekers, these three technology-driven options are poised to change the face of education.In this paper we present various applications of artificial intelligence and big data emphasis in education and its relation to technology, in particular relating to AI technology. We present trends and issues in this area exemplified by research projects and characterize various applications and scenarios in order to situate different modelling options for Artificial Intelligence and Big data in Education.

1 INTRODUCTION

Due to latest advancement of technologies and increase of data generated, big data plays a big role in managing and dealing with this data which comes from different sources and in form of semi-structured, structured and unstructured form. The innovation in technology combined with variety of data techniques have now provided the mechanism to deal with wide spectrum of issues that appear during the process of data collection and also during working with large volume, variety and velocity of data[1]. Everyday lots of data is being generated on regular basis in big magnitude, every instant of time in variety of forms (numbers, text, audio etc.) from multiple sources including systems, sensors, mobile devices, digital processes, and social media.

As education is need of society because the education plays the vital role in building the society, the quality of education determines standard of society.

The educational quality helps to empower the society in all aspects of bringing new thoughts, new technologies etc. to build a innovative platform for learning where it is easy for both students and teachers to share knowledge and give a new means to quality education [4]. There are a number of effective teaching and learning methodologies and according to the traditional approach of education which is the most in practice today, we have a

particular course structure or syllabus, subject wise textbooks and reference books where teachers teach in lectures, using board or presentations, lesson plans, tutorials etc. and the assessment of student‟s performance is done using assignment, exams etc.

To sustain in this dynamically changing technological world and for a quality education, student centered learning process is necessary where student will have anytime access to required information by the use of web as a source. Students can learn anytime and at any place and select their own experts with which they will be comfortable to work and form a bridge between students and teacher o share extensive knowledge giving shape to quality education.

Examples of different types of technologies used in education are mobile devices and apparatuses, teleconference and remote access systems, educational platforms and services and other that students, teachers, academic faculty, evaluation specialists, researchers and decision-makers in education interact with and use in an effort to impact and improve teaching and learning but also to realistically reflect in the learning stage the usage of modern technologies used in real settings. The interaction with these technologies generates large amounts of data that range from an individual access

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log file to an institutional level activity.The primary idea behind Big Data is the application of information tools to pave way for data analysis and extract useful information for better estimation, planning and judgment in any business process [2].

2 ARTIFICIAL INTELLIGENCE AND BIG DATA IN EDUCATION

Artificial Intelligence and Big data are changing the nature of industries from transportation to finance, and education is no different with the respect of personalized learning quickly become a reality. As more and more of a student‟s education is experienced through a computer, data on their educational progress can be collected, leading to more personalized learning plans while assisting the teacher in identifying problem areas for students. While artificial intelligence and big data in education might appear unnerving for some, the benefits are too great to ignore.

“MIT‟s plans for an AI-focused college is reflective of the technology‟s increasing influence and its role in changing the way businesses operate. As well as educating students, we also need to provide training to upskill existing workforces as AI technologies create new opportunities for those trained in it.”

Fig: 1 Artificial Intelligence and Big data for education

2.1 The Future Educational Trends Stemming From Artificial Intelligence Educational professionals are all set to unleash the capabilities of Artificial Intelligence (AI) so that they can help students to better interact with their surroundings in their pursuit for knowledge. Because of the fact that AI- embedded computers incorporate the

primal principles of education namely:

problem-solving, learning and reasoning, it is but natural for AI to have a say in the sphere of imparting knowledge.Researchers can also use processed information to identify the trends in education. They can then recommend changes to learning styles that suit a particular group of students in a class.

2.2 Applications of AI and Big data

The major applications of AI and Big data Enables Better Decision-Making and analyzing results to give better output.When schools store, collate and analyze volumes of information on a regular basis, they will be in a better position to come up with learning methodologies and goals that are practicable. Their decision- making abilities are strengthened when information is presented as a blend of descriptive data, research inferences and the findings from educationalists. Using this information coming from different quarters, schools will be in a favorable position to improve their teaching practices so as to bring in a greater relevance to education. However, the key is to have knowledge about how to use Big Data.

 AI applications can perform the basic tasks like grading, attendance and timetable making – It means every side-task, which teachers have to do, can be given to machines to improve the education level by allowing teachers to read more.

 This one is most fascinating. Imagine each of us having a virtual mentor.

Lesser mistakes, right? The AI tutors will surely solve many problems by helping us in decision-making. And guess what; their statistics will be more precise than your imagination.

 You may find the robots, checking the classroom‟s environment to detect issues such as temperature, pollution, etc.

 We implement Artificial Intelligence to perform course analysis by comparing it will other similar curriculums, in order to detect the area where you can improve it by consulting students from making the career choices. Well, these AI machines will have whole

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behavior and academic performance data about the students along with an excellent database of professions.

 The way of finding information will not remain as complex (and boring) as it currently is. AI will allow easy and hence, effective, interactive with the data for every learner. Interest will be to see the teachers. They will probably tackle the AI tutors, train them, and see if any student needs further assistance.

 AI can also be used in teacher training and with its data-powered mechanism, it will be self-sufficient.

2.3 Breaking peak advantages of AI and Big data in Education field

Machine learning

Machine learning allows computers to reason and make predictions about situations they have not encountered previously. One such application is a pattern matcher or a classifier. The machine learner takes a set of inputs describing the object, and tries to determine to which category the objects belongs. This can be applied, for example, to stereotypical student models [10]. A slightly more general view is to think of a machine learner as an automatic model generator. A linear regression is an example of this concept:

the goal is to determine a function that best predicts the environment. A possible use of such a machine learner could be to automatically derive the equations used by Shute [12] for updating a student‟s knowledge. A common objection to machine learning techniques is they need significant training, and require fast processors to run.

With the advent of web-based and networked ILEs, it is possible to gather training data from a large pool of users.

And with falling prices, the objection that a fast computer is required is quickly becoming moot.

2.4 Determining student goals

One way to problem solving of college students is to determine what problem- solving strategy the student is using currently. In many physics problems, there are multiple valid solution techniques [13].

Traditionally, rule-based systems have had difficulties with multiple solution paths.

The search problem of determining which production rules should fire can be expensive. Plan recognition strategies have also been used, but there are difficulties with integrating the student‟s prior knowledge [14].

2.5 Curriculum sequencing

In addition to reasoning at a micro-level about student actions, it is possible to use Bayesian networks to reason at a coarser grain size. One such use of Bayesian nets is determining a student‟s level of competence within a domain [5]. To do this, Collins et al. constructed a BN that represents a hierarchy of skills for arithmetic. The lowest level of the network contains the actual test questions, the next level arithmetic theory and arithmetic skill. This hierarchy can continue for a number of levels. The semantics implied by the links are „„is a part of the knowledge for.‟‟ So if a question points to an arithmetic skill, the implication is that question tests the student‟s skill at arithmetic. This hierarchy easily maps to a classical domain network, which makes design simpler. Also, it reduces the computational complexity of performing calculations with the Bayesian network.

Fig: 2 Advantage of AI and Big data in Education

2.6 Role of Artificial Intelligence and Big data in enhancing the development of literacy

Artificial intelligence is not only limited to computers and communication technology but is beneficial for other platforms as well.

Learning based on technologies with artificial intelligence and big data are referred as e-learning platforms where with the help of online experts and teachers, a

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learner can share and access any information related to its work and course studies.

E-learning platforms: Guenaneche and Radigales, stated that e-learning platforms integrate different tools including management tools, evaluation, monitoring, communication tools and others. The software application provides technological support for learners and academics to enhance teaching and learning activities.

The platforms can facilitate e-learning activities or a combined mode of both conventional and e-learning activities for teaching and learning[7]. According to Piotrowski, e-learning platforms provide a consolidated support system for communication, delivery, creation, collaboration, assessment and organization for teaching and learning activities [9].

Fig: 3 Benefits of E-learning platforms Benefits and challenges of e-learning platform: Social equity, economic competitiveness, multimedia-rich content, accessibility, financial benefits, avoiding the commute to campus, improved access to information, lifelong learning and convenience of time and place and flexibility are outlined as benefits of e-learning [11].

2.7 Merits of e-learning platforms

 These platforms makes it easy for the user to access e-learning programs from anywhere and at any time due to wide network.

 Information can be shared with other people by using cloud computing techniques

 Cloud plays very important role to store and exchange the information

with the help of privacy preservation techniques.

 Learner can learn anything without teacher from anywhere i.e. distance learning mechanism.

Benefits of e-learning in teaching process:

The direct benefits of using an e-learning platform by lecturers, for teaching and learning activities were established. The following benefits were identified:

 C Ease of access to information and simple dissemination of study materials

 C Improved communication between the lecturer and student

 C Simplified ways of monitoring of student activity through support of both synchronous and asynchronous communication

 C Promote student independence

 C Improved use of computer technology

 C Efficient student record management 2.8 Role of Artificial Intelligence and Big data in Higher education

Nowadays, artificial intelligence and big data are playing very significant role in higher studies or in research, technical institutions using these advanced technologies to improve skills of students in various areas and also managing and analyzing this huge data generated from various sources to make better predictions.

2.9 Artificial Intelligence as a change agent

Artificial intelligence plays a vital role in standarzing the quality of education. Use of Artificial intelligence helps to transform teacher centered learning to competency based learning. Educational board and universities should choose artificial intelligence as an advanced learning technology.

3 CONCLUSIONS AND FUTURE WORK Latest technologies such as Artificial intelligence and Big data have been proved successful to play an important and efficient role for students as well as for

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teachers in education sector, starting from school students- with smart classes and labs enriched with AI and automation work to students pursuing higher studies with AI and big data playing major role in helping them with their research studies and even teachers communicating with their students in no time helping them with their doubt sessions. In future, a method or framework could be proposed with the real time implementation of these technologies.

References

1. Oliver Marsh, Lajos MaurovichHorvat, Dr Olivia Stevenson “Big Data and Education: What‟s the Big Idea? “ UCL Policy Briefing -September 2014.

2. Michael Yao-Ping Peng, Sheng-Hwa tuan, Feng - Chi Liu “Establishment of Big Data Application for Education Industry”, 2017 2nd International Conference on Image , Vision and Computing.

3. Adomavicius, G. and Tuzhilin, A., Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions, IEEE Transactions on Knowledge and Data Engineering, pp. 734-749, 2005.

4. CSI Big data magazine „Role of ICT in Education

„Page 29 Introduction

5. J. Collins, J. Greer, and S. Huang. Adaptive assessment using granularity hierarchies and Bayesian nets. In Proceedings of Intelligent Tutoring Systems, pages 569-577, 1996.

6. Romero C, Ventura S. Educational data mining:

a survey from 1995 to 2005. Expert Systems with Applications. 2007;33(1):135–46.

7. Guenaneche, H.C. and F.G. Radigales, 2008. E- learning platforms: Moodle and Dokeos.

University of Madrid, Spain.

http://www.it.uc3m.es/rueda/lsfc/trabajos/cu rso07-08/Elearning% 20platforms- HomeroCanales-FernandoGarcia.pdf

8. Piotrowski, M., 2010. What is an e-Learning Platform? In: Learning Management System Technologies and Software Solutions for Online Teaching: Tools and Applications, Kats, Y. (Ed.).

Chapter 2, IGI Global, Zurich, Switzerland, ISBN-13: 9781615208548, pp: 20-36.

9. J. Kay. Lies, damned lies and stereotypes:

Pragmatic approximation of users. In Proceedings of Fourth International Conference on User Modeling, pages 175-184, 1994.

10. Bates, A.W., 2005. Technology, e-Learning and Distance Education. Routledge, London.

11. V. Shute. Smart evaluation: Cognitive diagnosis, mastery learning and remediation. In Proceedings of Artificial Intelligence in Education, pages 123-130, 1995.

12. A. S. Gertner, C. Conati, and K. VanLehn.

Procedural help in andes: Generating hints using a bayesian network student model. In Fifteenth National Conference on Artificial Intelligence, pages 106-111, 1998.

13. C. Conati, A. Gertner, K. VanLehn, and M.

Druzdel. On-line student modeling for coached problem solving using Bayesian networks. In Proceedings of the Seventh International Conference on User Modeling, pages 231-242, 1997.