Machine Learning Applications in Educational Institutions
1 Introduction
Machine Learning is a subdivision of Artificial Intelligence (AI), whereby a system can intelligently learn new processes and enhance its operations from its experience. Therefore, machine learning creates the opportunity for intelligent systems to make decisions based on the activities occurring within their environment (Gummadi, 2020). The use of machine learning approaches such as pattern matching also enables a system to identify processes that are identical to previous ones, and to apply their experience and intelligence in handling such processes. Machine learning can be applied in various industries, one of which is the education industry. This paper examines the potential benefits of applying machine learning algorithms, such as pattern recognition, to teaching practices. Subsequently the future trend of machine learning in education is discussed, to highlight the possible applications of machine learning in educational institutions.
2 Machine Learning in Education
According to Nieto et al. (2019), face-to-face interactions and manual processes within an education setting often encounters major obstacles. For instance, both administrative and students’ information is stored on decentralised databases, which could make data processing or retrieval a challenging process. Also, a generic approach is often used to teach students, whereby teachers use the same method of teaching for a wide range of learners. This has resulted in students being compelled to adapt their learning styles to fit their school’s lesson plans and curriculum. Similarly, teachers are not completely cognisant of specific students’ needs and the potential solutions for enhanced learning (Hafeez and Ahmed, 2019). This could have a negative impact on learning and teaching practices and can also have an impact on students’ learning experiences. Thus, the use of effective computation programmes is essential to improve the existing processes in educational institutions.
Machine learning has been identified to be one of the technological solutions for these challenges. The use of algorithms like supervised algorithms has been suggested as effective solutions for enhanced teaching processes in educational institutions (Nieto et al., 2019). Particularly, the adoption of machine learning has been proposed to aid in effective and tailored learning processes (Hafeez and Ahmed, 2019). Some of the applications of machine learning in educational institutions are further discussed in the next subsection.
2.1 Predictive Analytics
Predictive analytics refers to the use of data analytics to forecast future trends, based on chronological data and analytics methods (Edwards, 2019). Thus, the use of modern predictive analytic methods enables an organisation to apply both current and historical information to consistently predict trends and outcomes (Hafeez and Ahmed, 2019).
The application of predictive analytics can aid both teachers and educational institutions with effectively analysing student’s performance. Furthermore, teachers can effectively analyse trends in academical outcomes of their students to highlight any weaknesses in their teaching methods (Nafea, 2018). The use of machine learning approaches such as the support vector system can aid with identifying students that are struggling and require additional support (Hodges and Mohan, 2019). Other approaches like neural network and linear regression have also been proposed as potential solutions for effectively forecasting the academic outcomes of students studying different programs in their schools (Hafeez and Ahmed, 2019).
2.2 Personalised and Tailored learning processes
Machine learning is adaptable enough to facilitate every student’s learning, irrespective of their learning requirements. Algorithms like deep learning algorithms can learn the way students utilise information, and machine learning enables them to only progress to the next level of learning when they have completely understood the contents of their current lesson plan (Nafea, 2018). Thus, students are not neglected. It also offers teachers the opportunity to exclusively observe each student and assist them in the subjects they find challenging.
2.3 Machine Learning Programs as Assistive Tutors
Machine learning can also be used to create assistive tutors. For instance, deep learning applications are gradually becoming significant platforms for educational institutions. Examples of such tutors include Duolingo, which is aimed at enhancing the learning process by providing personalised support and prompt feedback (Webb et al., 2020). Duolingo also offers a platform for studying a new language and adjusts to learners’ skills by using their information and deep learning algorithms to forecast areas where they might require support. Another deep learning application, known as Amira, is a reading aide used by students in primary schools that aids them in evaluating their reading skills using deep learning algorithms. These machine learning applications have been enhanced with modern artificial intelligence capabilities and comprise of libraries that are constantly developed to aid with students’ learning (Garcia Botero et al. 2019).
2.4 Enhanced Decision-Making Processes
Decision making in learning institutions often impact strategies, procedures, and activities that such institutions implement. Machine learning offers an approach to effective decision making, as it uses algorithms to evaluate data and offers a more comprehensive overview of collected information (Nieto et al., 2019). It is also pertinent for educational institutions, as features such as pattern recognition can aid in identifying certain factors, such as courses that are more popular, or students that will potentially drop out of their courses. Furthermore, the accurate projection of learners’ academic conduct during decision-making procedures could have a major impact on admission policies in an educational institution (Chen and Do, 2014).
2.5 Enhanced Grading Systems
Machine learning programmes aid with general tasks, such as grading students work, thereby decreasing the amount of time required by teachers. Also, they improve the effectiveness of an institution’s grading system, as a huge volume of students’ work can be graded swiftly (Nafea, 2018). Thus, it simplifies tedious responsibilities, as conventional methods often involve teachers spending a lot of time on monotonous and tiresome tasks. Machine learning automates such tasks, thereby enabling teachers to focus on other significant tasks like ensuring their students completely understand their learning content (Asthana and Hazela, 2020)
3 Conclusion
Artificial intelligence, combined with machine learning algorithms like deep learning and supervised learning, is projected to experience continuous development, with the opportunity for more enhanced systems that can modify, learn, and forecast various activities. Future developments could also include a blend of sophisticated algorithms and integrated huge datasets, enabling easy access and analysis of collected data. Though the application of machine learning algorithms in the educational sector is still in its early stages, the effectiveness of these algorithms for data analytics and pattern recognition cannot be ignored. It can, therefore, be deduced that there is a lot of potential for the application of machine learning algorithms and AI-based systems in the education industry. With access to such opportunities, educational institutions would have the opportunity to offer more tailored and student-focused teaching practices. Therefore, it can be concluded that the application of machine learning approaches would enhance the current practices of educational institutions.
References
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