Data Mining Practical Machine Learning Tools and Techniques

La calidad del servicio educativo está relacionada directamente con el logro perfil profesional, para ello no basta con desarrollar sesiones de aprendizaje; es importante concretar acciones de seguimiento y acompañamiento para evitar el riesgo académico, de allí la importancia de experimentar la influencia de un programa de tutoría y consejería. El objetivo del estudio fue demostrar la influencia de un programa de tutoría y consejería en el desempeño de los estudiantes universitarios. La investigación se caracterizó por el enfoque cuantitativo, con alcance explicativo; la guía de investigación se alineó al diseño cuasiexperimental. En este estudio participaron 6 tutores, 1 psicólogo y 160 estudiantes de cuatro carreras profesionales de educación (Huaraz, Perú). Se aplicó un cuestionario para recoger información general como el ámbito personal, familiar, social, académico, de salud, y el vocacional. Los resultados de la puntuación del desempeño académico, así como los resultados de percepción señalan que estadísticamente es significativa la aplicación de un programa de intervención tutorial que contribuye a mejorar el desempeño de los estudiantes. En virtud a ello, se llegó a la conclusión de que existe influencia directa del programa de tutoría y consejería en el rendimiento académico de los estudiantes que participaron en el estudio.

Tutoring and counseling program in the performance of students from a professional school of education

In this entry, we define Intelligent Tutoring Systems (ITSs) and present a description of their core components. We outline a history of the development of ITSs with a focus on key issues that have driven change and innovation in ITSs from their inception to present day. We also present a brief case study on a specific ITS, Inq-ITS (Inquiry Intelligent Tutoring System; inqits.com), that is currently in use in 50 states in the US as well as many countries globally (12+) for the assessment of students' science competencies, and in turn, scaffolding students' science competencies. 

Intelligent tutoring systems: a history and an example of an ITS for science

In recent days, the research on student’s intelligence level modelling is a challenging Artificial Intelligence (AI) task, which gains more attraction because it provides actionable insights to the tutor by analyzing the intelligence level of the learners. Each learner’s knowledge, comprehension, and intellectual capacities are unique. It is critical to identify these capacities and provide learners, particularly slow learners, with the necessary knowledge. Cognitive Performance Test (CPT) is an essential component for assessing the knowledge level of students. The reasoning level or coefficient deals with the analysis of the thinking capability in a logical way. It also reflects the child’s learning potential. The main aim of the proposed system is to design a Cognitive Knowledge Representation Model (CKRM), which fuses Cognitive Performance Metrics (CPM) calculation and Reasoning Coefficient Calculation (RCC) algorithms to assess the student’s intelligence level. The result of the proposed system is stratification of students to three different ranges of reasoning coefficient. The CKRM consists of the following phases: data collection, statistical Exploratory Data Analysis (EDA), model building and analysis, which involve the assessment of the knowledge level using CPT and calculation of reasoning coefficient using First Order Logic (FOL), and finally model evaluation using cognitive evaluation metrics. CPM and RCC algorithms have been proposed in this paper to calculate the student’s reasoning coefficient by using the forward chaining FOL inference engine. The dataset is a real time data which consists of the academic and cognitive performance details of school students from classes 1 to 6 for the year 2019 to 2020. The academic data are collected from the Educational Management Information System (EMIS) maintained by the school. The cognitive performance data are collected by conducting the tests for the students using the memory training application called Lumosity. The proposed system’s performance is evaluated using ten Machine Learning (ML) algorithms in which the Quadratic Discriminant Analysis achieved an accuracy of 0.97 for classes 1, 2, and 3. For classes 4, 5, and 6, nearly twelve ML algorithms are evaluated in which Random Forest (RF) Classifier achieved an accuracy of 0.98. Six math expert committee teachers concluded that the reasoning coefficient value was acceptable with an average accuracy of 0.92 for classes 1, 2, 3 and 0.9 for classes 4, 5, 6. In comparison to the pre-existing models employed in the prior research, it was determined that the created CKRM (academic and cognitive) was superior. The cognitive metrics such as taskability, Response Time (RT), knowledge capacity and utilization has also been evaluated. The average values of taskability, RT, knowledge capacity and knowledge utilisation are 0.85, 0.81, 0.55, and 0.44. The ultimate goal is to make customised teaching easier; hence, this article involves determining a student’s cognitive level by estimating their reasoning coefficient. The suggested approach analyses and categorises students’ cognitive abilities, such as memory, reasoning, problem solving, thinking, and logical reasoning, using three different reasoning coefficients. This approach assists teachers in determining the degree of intelligence of their students. 

Design of a Cognitive Knowledge Representation Model to Assess the Reasoning Levels of Primary School Children

The evolution of educational systems is closely tied to technological advancements, particularly the emergence of machine learning. This technology offers a sophisticated system capable of predicting, explaining, and influencing behavior. Many efforts have aimed to integrate machine learning into education, focusing on specific cases using ad-hoc models. This paper introduces an intelligent educational system that relies on data-driven student models, aiming to surpass the limitations of these ad-hoc systems. The approach outlined in this endeavor adopts a comprehensive and methodical modeling methodology centered on machine learning techniques. By employing Long Short-Term Memory (LSTM), the proposed approach enables predictive student models based on historical educational data. The effectiveness of this method was tested through experimentation on an intelligent tutoring system using 5-fold cross-validation, revealing that the smart educational system achieved a remarkable 96% accuracy rate. Furthermore, a comparison between the importance scores of features with and without the student models demonstrated the practicality and effectiveness of the proposed methodology.

Empowering Learning through Intelligent Data-Driven Systems

Machine learning-based modeling technology has recently become a powerful technique and tool for developing models for explaining, predicting, and describing system/human behaviors. In developing i...

Machine Learning-Based Student Modeling Methodology for Intelligent Tutoring Systems:

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Machine Learning-Based Student Modeling Methodology for Intelligent Tutoring Systems: