Machine Learning in Education Sector

-

 Machine Learning is the next trending concept in various fields related to education. It involves the study of learning processes and computer modelling in different contexts. The authors in (Carbonell, 1983) rightly stated that ML can be used in task-oriented studies, theoretical analysis and cognitive simulation. Machine Learning in Education field falls in the category of task-oriented studies since it involves developing and analyzing learning systems to improve performance of education institutions through predetermined set of actions.

Classification Techniques in Education

The main classification techniques used in education include Artificial Neural Networks, Decision Trees, Logical Regression and Support Vector Machine. As stated by Nieto et. al. in the paper, the decision making model offers support in decision making on various aspects in educational industry. ML can be useful in increasing student retention and mitigating dropout rate, strategic planning using the knowledge of future, managing interventions and enhancing education institutions quality indicators.

Benefit of ML to Education Institutions in Decision Making

The issue faced by various institutions is that they have to rely on decision making arbitrarily, lacking any logical reasoning. The factors responsible for such random decision making are lack of past experiences, delayed decisions, incomprehensive observation and constrained academic impact of the decision. ML can be beneficial in making strategic decision related to student retention, institution resource planning, curriculum planning and managing dropout. Approaches and models based on ML can help the institutions to perform reliable predictive analytics through in-depth analysis of hidden patterns.

AI predicting Slow Learners

The most interesting service of AI in education is how it can predict slow learners. ML combined with data mining can be used in such prediction. The authors of (Kaur, 2015) state that slow learners can be determined using classification and regression technique of data mining prediction model and ML techniques of SVM and Decision Tree can be used to improve the prediction outcomes. Another area where combination of ML and DM is effective includes enhancing online education. The global crisis of 2020 has led to an urgent need of online education. It requires Information and Communication Technologies in order to support student to access education while remaining confined geographically. The authors of (Villegas, 2020) state that ML can learn from past experiences with a specific objective. ML as a tool can predict, model and control systems while performing in-depth analysis of data. The authors have proposed two main strategies for implementing ML model in education. Stakeholders are given tasks in order to generate data for the tool so that it can learn about the task which it has never performed. Supervised learning technique shall require the tool to be introduced to previous datasets so that it can learn from recognition patterns and identify accurately or learn through regression for predicting accurate outcome.

Other Benefits

Machine Learning algorithms can be deployed to predict graduation rate, student retention and drop-out rates from real data in higher educational institutions. The techniques of logistic regression, decision tree and random forest may be used to analyze information and achieve better understanding of data contained in a particular context. The accuracy and analysis of these techniques shall help in evaluating the best outcome. These techniques can identify students who may not graduate. They can further predict academic performance of students as well as their drop-out rate. The results shall offer insightful decision making in various other aspects of education like curriculum designing, resource planning, etc. Such predictions early in the strategic planning of the institutions will support governance of the institutions.

References

Carbonell, J., G., Michalski, R., S., Mitchell, T., M., “1 - An Overview of Machine Learning”, Pages 3-23, ISBN 9780080510545, 1983, https://doi.org/10.1016/B978-0-08-051054-5.50005-4.

Villegas-Ch, W.; Román-Cañizares, M.; Palacios-Pacheco, X. Improvement of an Online Education Model with the Integration of Machine Learning and Data Analysis in an LMS. Appl. Sci. 2020, 10, 5371.

Y. Nieto, V. Gacía-Díaz, C. Montenegro, C. C. González and R. González Crespo, "Usage of Machine Learning for Strategic Decision Making at Higher Educational Institutions," in IEEE Access, vol. 7, pp. 75007-75017, 2019, doi: 10.1109/ACCESS.2019.2919343.

Comments

Post a Comment

Popular posts from this blog

IoT: Device Vulnerability & Security Concerns in Wearable Devices

-
Wearable IoT have broaden and improved the perspective of individuals about their surroundings. A successful IoT device has to be affordable as well as reliable. It should not interfere with other devices and should incorporate mechanisms to remain immune from interferences. It should further ensure effective battery life with perfect functionality and energy efficiency. However, the risks involved in attaining this ideal scenario are many. IoT include a plethora of devices or things like vehicles, smartphones, televisions, wearable devices like fitness tracking devices, smartwatch and Google Glass, etc. These devices are embedded with software applications, sensors and other components for receiving and sending data. A colossus of connected devices is created by inter-connectivity of those devices. This connectivity offers magnificent benefits to people’s lives but it entails certain security and privacy concerns. The most conspicuous among them are the wearable devices which are gain...
Read more

Wireless Sensor Network Protocol: Sensor Protocol for Information via Negotiation (SPIN)

-
Image
 A wireless sensor network is formed using multiple small sensors. Such a network is a self- organized infrastructure-less wireless network that monitors environmental conditions on its own and transmit data in a synchronized manner to main location called sink which analyses the data. Sink addresses the queries generated by users by collecting results and providing required information. Routing protocols play an important role in producing efficient and interruption less communication within the nodes of the WSN. Applications using WSN demand quick data transmission with minimum interruption. The service, performance and reliability of the WSN depends on the choice of its routing protocol. This article discusses Sensor protocol for information via negotiation (SPIN) which allow exchange of data effectively over a WSN. Sensor Protocol for Information via Negotiation (SPIN) This routing protocol is basically for exchanging data about the sensor data in network operation. It is a neg...
Read more

Artificial Intelligence in Patient Health Monitoring System

-
 Artificial Intelligence is the study and implementation of actions on computational devices which require human intervention. AI captures the intelligence and uses it in computation technology. Robert Plant stated “if Socrates is a man and all men are mortal, then it can be inferred that Socrates is mortal”. Similarly, AI works on the principle of transitive dependency. Human activities are recorded as the acceptable standard of decision making. Machine intelligence is built over those records and decisions are made by the intelligent machine which work very similar to humans. Basic Benefits to Healthcare workers and patients Software applications on Health care system are advantageous to the healthcare facilities, healthcare workers and patients. Some of the benefits of AI in health domain are The information can be quickly accessed with reduced errors. Healthcare workers can coordinate better among themselves reduced need of visiting healthcare facility AI offers better educatio...
Read more