Predicting Student Performance Using Data Mining and Learning Analytics Techniques: A Systematic Literature Review

Student course grade prediction using the random forest algorithm: Analysis of predictors' importance

BACKGROUND

Universities need to find strategies for improving student retention rates. Predicting student academic performance enables institutions to identify underachievers and take appropriate actions to increase student completion and lower dropout rates.

METHOD

In this work, we proposed a model based on random forest methodology to predict students' course performance using seven input predictors and find their relative importance in determining the course grade. Seven predictors were derived from transcripts and recorded data from 650 undergraduate computing students.

RESULTS

Our findings indicate that grade point average and high school score were the two most significant predictors of a course grade. The course category and class attendance percentage have equal importance. Course delivery mode does not have a significant effect.

CONCLUSION

Our findings show that courses students at risk find challenging can be identified, and appropriate actions, procedures, and policies can be taken.

Use of machine learning to assess factors affecting progression, retention, and graduation in first-year health professions students in Qatar: a longitudinal study

BACKGROUND

Across higher education, student retention, progression, and graduation are considered essential elements of students' academic success. However, there is scarce literature analyzing these attributes across health professions education. The current study aims to explore rates of student retention, progression, and graduation across five colleges of the Health Cluster at Qatar University, and identify predictive factors.

METHODS

Secondary longitudinal data for students enrolled at the Health Cluster between 2015 and 2021 were subject to descriptive statistics to obtain retention, progression and graduation rates. The importance of student demographic and academic variables in predicting retention, progression, or graduation was determined by a predictive model using XGBoost, after preparation and feature engineering. A predictive model was constructed, in which weak decision tree models were combined to capture the relationships between the initial predictors and student outcomes. A feature importance score for each predictor was estimated; features that had higher scores were indicative of higher influence on student retention, progression, or graduation.

RESULTS

A total of 88% of the studied cohorts were female Qatari students. The rates of retention and progression across the studied period showed variable distribution, and the majority of students graduated from health colleges within a timeframe of 4-7 years. The first academic year performance, followed by high school GPA, were factors that respectively ranked first and second in importance in predicting retention, progression, and graduation of health majors students. The health college ranked third in importance affecting retention and graduation and fifth regarding progression. The remaining factors including nationality, gender, and whether students were enrolled in a common first year experience for all colleges, had lower predictive importance.

CONCLUSIONS

Student retention, progression, and graduation at Qatar University Health Cluster is complex and multifactorial. First year performance and secondary education before college are important in predicting progress in health majors after the first year of university study. Efforts to increase retention, progression, and graduation rates should include academic advising, student support, engagement and communication. Machine learning-based predictive algorithms remain a useful tool that can be precisely leveraged to identify key variables affecting health professions students' performance.

Midwifery learning and forecasting: Predicting content demand with user-generated logs

Every day, 800 women and 6700 newborns die from complications related to pregnancy or childbirth. A well-trained midwife can prevent most of these maternal and newborn deaths. Data science models together with logs generated by users of online learning applications for midwives can help improve their learning competencies. In this work, we evaluate various forecasting methods to determine the future interest of users for the different types of content available in the Safe Delivery App, a digital training tool for skilled birth attendants, broken down by profession and region. This first attempt at health content demand forecasting for midwifery learning shows that DeepAR can accurately anticipate content demand in operational settings, and could therefore be used to offer users personalized content and to provide an adaptive learning journey.

Prediction of Graduate Learners' Academic Achievement in an Online Learning Environment Using a Blended Trauma Course

BACKGROUND

The concepts of online and blended education came into the limelight in the 19th century. Over time, the concepts expanded and reached a peak in 2021 in response to the COVID-19 lockdown. One of the challenges is the monitoring of the performance of distant learners. In face-to-face courses, an instructor can easily identify struggling learners during the regular meetings.

AIM OF THE STUDY

This study explored variables that can predict the academic achievement of learners early in online learning environments. Although there was no consensus, the factors were still hypothesized as predictors for academic achievement.

METHODS

A quasi-experimental study was conducted to test the hypothesis. Thirty-three graduate learners were enrolled in a blended trauma course. The learners' age, their previous experiences in online education, pre-test scores, and the number of logs to the online platform were studied. These elements were considered as predictors of academic achievement in the online aspect of the course.

RESULTS

The findings revealed that there was no statistically significant correlation between the age, the previous experience in online education, the pre-test scores, and the number of logs in the first two weeks. However, there was a statistically significant correlation between the number of logs into the online platform in the first three weeks of study and the learners' academic achievement. Additionally, the number of logs in the first three weeks was a statistically significant predictor for academic achievement in online education. This early prediction can help instructors to identify and support struggling learners.

CONCLUSION

The records of the online activity of learners in the first three weeks of study can help in early prediction of their academic achievement. Age, previous online education, and pretest scores were not statistically significant predictors.

Predicting academic success of autistic students in higher education

LAYMEN SUMMARY

What is already known about the topic?Autistic youths increasingly enter universities. We know from existing research that autistic students are at risk of dropping out or studying delays. Using machine learning and historical information of students, researchers can predict the academic success of bachelor students. However, we know little about what kind of information can predict whether autistic students will succeed in their studies and how accurate these predictions will be.What does this article add?In this research, we developed predictive models for the academic success of 101 autistic bachelor students. We compared these models to 2,465 students with other health conditions and 25,077 students without health conditions. The research showed that the academic success of autistic students was predictable. Moreover, these predictions were more precise than predictions of the success of students without autism.For the success of the first bachelor year, concerns with aptitude and study choice were the most important predictors. Participation in pre-education and delays at the beginning of autistic students' studies were the most influential predictors for second-year success and delays in the second and final year of their bachelor's program. In addition, academic performance in high school was the strongest predictor for degree completion in 3 years.Implications for practice, research, or policyThese insights can enable universities to develop tailored support for autistic students. Using early warning signals from administrative data, institutions can lower dropout risk and increase degree completion for autistic students.

Recent advances in Predictive Learning Analytics: A decade systematic review (2012-2022)

The last few years have witnessed an upsurge in the number of studies using Machine and Deep learning models to predict vital academic outcomes based on different kinds and sources of student-related data, with the goal of improving the learning process from all perspectives. This has led to the emergence of predictive modelling as a core practice in Learning Analytics and Educational Data Mining. The aim of this study is to review the most recent research body related to Predictive Analytics in Higher Education. Articles published during the last decade between 2012 and 2022 were systematically reviewed following PRISMA guidelines. We identified the outcomes frequently predicted in the literature as well as the learning features employed in the prediction and investigated their relationship. We also deeply analyzed the process of predictive modelling, including data collection sources and types, data preprocessing methods, Machine Learning models and their categorization, and key performance metrics. Lastly, we discussed the relevant gaps in the current literature and the future research directions in this area. This study is expected to serve as a comprehensive and up-to-date reference for interested researchers intended to quickly grasp the current progress in the Predictive Learning Analytics field. The review results can also inform educational stakeholders and decision-makers about future prospects and potential opportunities.

Predicting Student Dropout and Academic Success

Higher education institutions record a significant amount of data about their students, representing a considerable potential to generate information, knowledge, and monitoring. Both school dropout and educational failure in higher education are an obstacle to economic growth, employment, competitiveness, and productivity, directly impacting the lives of students and their families, higher education institutions, and society as a whole. The dataset described here results from the aggregation of information from different disjointed data sources and includes demographic, socioeconomic, macroeconomic, and academic data on enrollment and academic performance at the end of the first and second semesters. The dataset is used to build machine learning models for predicting academic performance and dropout, which is part of a Learning Analytic tool developed at the Polytechnic Institute of Portalegre that provides information to the tutoring team with an estimate of the risk of dropout and failure. The dataset is useful for researchers who want to conduct comparative studies on student academic performance and also for training in the machine learning area.

Exploring Factors That Affected Student Well-Being during the COVID-19 Pandemic: A Comparison of Data-Mining Approaches

COVID-19-related school closures caused unprecedented and prolonged disruption to daily life, education, and social and physical activities. This disruption in the life course affected the well-being of students from different age groups. This study proposed analyzing student well-being and determining the most influential factors that affected student well-being during the COVID-19 pandemic. With this aim, we adopted a cross-sectional study designed to analyze the student data from the Responses to Educational Disruption Survey (REDS) collected between December 2020 and July 2021 from a large sample of grade 8 or equivalent students from eight countries (n = 20,720), including Burkina Faso, Denmark, Ethiopia, Kenya, the Russian Federation, Slovenia, the United Arab Emirates, and Uzbekistan. We first estimated a well-being IRT score for each student in the REDS student database. Then, we used 10 data-mining approaches to determine the most influential factors that affected the well-being of students during the COVID-19 outbreak. Overall, 178 factors were analyzed. The results indicated that the most influential factors on student well-being were multifarious. The most influential variables on student well-being were students' worries about contracting COVID-19 at school, their learning progress during the COVID-19 disruption, their motivation to learn when school reopened, and their excitement to reunite with friends after the COVID-19 disruption.

Student Dataset from Tecnologico de Monterrey in Mexico to Predict Dropout in Higher Education

High dropout rates and delayed completion in higher education are associated with considerable personal and social costs. In Latin America, 50% of students drop out, and only 50% of the remaining ones graduate on time. Therefore, there is an urgent need to identify students at risk and understand the main factors of dropping out. Together with the emergence of efficient computational methods, the rich data accumulated in educational administrative systems have opened novel approaches to promote student persistence. In order to support research related to preventing student dropout, a dataset has been gathered and curated from Tecnologico de Monterrey students, consisting of 50 variables and 143,326 records. The dataset contains non-identifiable information of 121,584 High School and Undergraduate students belonging to the seven admission cohorts from August–December 2014 to 2020, covering two educational models. The variables included in this dataset consider factors mentioned in the literature, such as sociodemographic and academic information related to the student, as well as institution-specific variables, such as student life. This dataset provides researchers with the opportunity to test different types of models for dropout prediction, so as to inform timely interventions to support at-risk students.

An Empirical Study on IPO Model Construction of Undergraduate Education Quality Evaluation in China from the Statistical Pattern Recognition Approach In NLP

Based on the analysis of 1497 samples from the survey of national undergraduate educational administrators, the IPO model of undergraduate education quality evaluation from the perspective of managers can be effectively verified. The quality of higher education needs the accountability of higher education and evaluation of student learning outcomes. The empirical studies show the effects on the various dimensions of quality provisions which were not the same. The main findings are that the input of undergraduate education and teaching can not only directly and positively predict the output of undergraduate education and teaching, but also can positively predict the output of undergraduate education and teaching through the partial mediating effect of the process of undergraduate education and teaching. The research suggests that under the given material conditions, it is essential to enhance the “soft input” of undergraduate education and teaching, to strengthen the process construction of undergraduate education and teaching, to enhance the process quality control in the implementation of humanistic care, to pay attention to the development effect of teachers and students and the development effect of school-running characteristics, and to promote the connotative development of colleges and universities. The study provides a practical framework, model, and guidelines that can be used for undergraduate education institutions to evaluate and enhance the performance to effectively work in society. The quality evaluation of undergraduate education needs to focus from the quality of students' learning outcomes to the comprehensive consideration of “input-process-outcome”.

Predicting students' performance in English and Mathematics using data mining techniques

This study attempts to predict secondary school students' performance in English and Mathematics subjects using data mining (DM) techniques. It aims to provide insights into predictors of students' performance in English and Mathematics, characteristics of students with different levels of performance, the most effective DM technique for students' performance prediction, and the relationship between these two subjects. The study employed the archival data of students who were 16 years old in 2019 and sat for the Malaysian Certificate of Examination (MCE) in 2021. The learning of English and Mathematics is a concern in many countries. Three main factors, namely students' past academic performance, demographics, and psychological attributes were scrutinized to identify their impact on the prediction. This study utilized the Orange software for the DM process. It employed Decision Tree (DT) rules to determine the characteristics of students with low, moderate, and high performance in English and Mathematics subjects. DT and Naïve Bayes (NB) techniques show the best predictive performance for English and Mathematics subjects, respectively. Such characteristics and predictions may cue appropriate interventions to improve students' performance in these subjects. This study revealed students' past academic performance as the most critical predictor, as well as a few demographics and psychological attributes. By examining top predictors derived using four different classifier types, this study found that students' past Mathematics performance predicts their MCE English performance and students' past English performance predicts their MCE Mathematics performance. This finding shows students' performances in both subjects are interrelated.

Accuracy comparison between statistical and computational classifiers applied for predicting student performance in online higher education

Educational institutions abruptly implemented online higher education to cope with sanitary distance restrictions in 2020, causing an increment in student failure. This negative impact attracts the analyses of online higher education as a critical issue for educational systems. The early identification of students at risk is a strategy to cope with this issue by predicting their performance. Computational techniques are projected helpful in performing this task. However, the accurateness of predictions and the best model selection are goals in progress. This work objective is to describe two experiments using student grades of an online higher education program to build and apply three classifiers to predict student performance. In the literature, the three classifiers, a Probabilistic Neural Network, a Support Vector Machine, and a Discriminant Analysis, have proved efficient. I applied the leave-one-out cross-validation method, tested their performances by five criteria, and compared their results through statistical analysis. The analyses of the five performance criteria support the decision on which model applies given particular prediction goals. The results allow timely identification of students at risk of failure for early intervention and predict which students will succeed.

A Predictive Model for Student Achievement Using Spiking Neural Networks Based on Educational Data

Student achievement prediction is one of the most important research directions in educational data mining. Student achievement directly reflects students’ course mastery and lecturers’ teaching level. Especially for the achievement prediction of college students, it not only plays an early warning and timely correction role for students and teachers, but also provides a method for university decision-makers to evaluate the quality of courses. Based on the existing research and experimental results, this paper proposes a student achievement prediction model based on evolutionary spiking neural network. On the basis of fully analyzing the relationship between course attributes and student attributes, a student achievement prediction model based on spiking neural network is established. The evolutionary membrane algorithm is introduced to learn hyperparameters of the model, so as to improve the accuracy of the model in predicting student achievement. Finally, the proposed model is used to predict student achievement on two benchmark student datasets, and the performance of the prediction model proposed in this paper is analyzed by comparing with other experimental algorithms. The experimental results show that the model based on spiking neural network can effectively improve the prediction accuracy of student achievement.

Understanding Utility and Privacy of Demographic Data in Education Technology by Causal Analysis and Adversarial-Censoring

Education technologies (EdTech) are becoming pervasive due to their cost-effectiveness, accessibility, and scalability. They also experienced accelerated market growth during the recent pandemic. EdTech collects massive amounts of students’ behavioral and (sensitive) demographic data, often justified by the potential to help students by personalizing education. Researchers voiced concerns regarding privacy and data abuses (e.g., targeted advertising) in the absence of clearly defined data collection and sharing policies. However, technical contributions to alleviating students’ privacy risks have been scarce. In this paper, we argue against collecting demographic data by showing that gender—a widely used demographic feature—does not causally affect students’ course performance: arguably the most popular target of predictive models. Then, we show that gender can be inferred from behavioral data; thus, simply leaving them out does not protect students’ privacy. Combining a feature selection mechanism with an adversarial censoring technique, we propose a novel approach to create a ‘private’ version of a dataset comprising of fewer features that predict the target without revealing the gender, and are interpretive. We conduct comprehensive experiments on a public dataset to demonstrate the robustness and generalizability of our mechanism.

Learning analytics dashboard: a tool for providing actionable insights to learners

This study investigates current approaches to learning analytics (LA) dashboarding while highlighting challenges faced by education providers in their operationalization. We analyze recent dashboards for their ability to provide actionable insights which promote informed responses by learners in making adjustments to their learning habits. Our study finds that most LA dashboards merely employ surface-level descriptive analytics, while only few go beyond and use predictive analytics. In response to the identified gaps in recently published dashboards, we propose a state-of-the-art dashboard that not only leverages descriptive analytics components, but also integrates machine learning in a way that enables both predictive and prescriptive analytics. We demonstrate how emerging analytics tools can be used in order to enable learners to adequately interpret the predictive model behavior, and more specifically to understand how a predictive model arrives at a given prediction. We highlight how these capabilities build trust and satisfy emerging regulatory requirements surrounding predictive analytics. Additionally, we show how data-driven prescriptive analytics can be deployed within dashboards in order to provide concrete advice to the learners, and thereby increase the likelihood of triggering behavioral changes. Our proposed dashboard is the first of its kind in terms of breadth of analytics that it integrates, and is currently deployed for trials at a higher education institution.

Toward Predicting Student’s Academic Performance Using Artificial Neural Networks (ANNs)

Student performance is related to complex and correlated factors. The implementation of a new advancement of technologies in educational displacement has unlimited potentials. One of these advances is the use of analytics and data mining to predict student academic accomplishment and performance. Given the existing literature, machine learning (ML) approaches such as Artificial Neural Networks (ANNs) can continuously be improved. This work examines and surveys the current literature regarding the ANN methods used in predicting students’ academic performance. This study also attempts to capture a pattern of the most used ANN techniques and algorithms. Of note, the articles reviewed mainly focused on higher education. The results indicated that ANN is always used in combination with data analysis and data mining methodologies, allowing studies to assess the effectiveness of their findings in evaluating academic achievement. No pattern was detected regarding selecting the input variables as they are mainly based on the context of the study and the availability of data. Moreover, the very limited tangible findings referred to the use of techniques in the actual context and target objective of improving student outcomes, performance, and achievement. An important recommendation of this work is to overcome the identified gap related to the only theoretical and limited application of the ANN in a real-life situation to help achieve the educational goals.

On Developing Generic Models for Predicting Student Outcomes in Educational Data Mining

Poor academic performance of students is a concern in the educational sector, especially if it leads to students being unable to meet minimum course requirements. However, with timely prediction of students’ performance, educators can detect at-risk students, thereby enabling early interventions for supporting these students in overcoming their learning difficulties. However, the majority of studies have taken the approach of developing individual models that target a single course while developing prediction models. These models are tailored to specific attributes of each course amongst a very diverse set of possibilities. While this approach can yield accurate models in some instances, this strategy is associated with limitations. In many cases, overfitting can take place when course data is small or when new courses are devised. Additionally, maintaining a large suite of models per course is a significant overhead. This issue can be tackled by developing a generic and course-agnostic predictive model that captures more abstract patterns and is able to operate across all courses, irrespective of their differences. This study demonstrates how a generic predictive model can be developed that identifies at-risk students across a wide variety of courses. Experiments were conducted using a range of algorithms, with the generic model producing an effective accuracy. The findings showed that the CatBoost algorithm performed the best on our dataset across the F-measure, ROC (receiver operating characteristic) curve and AUC scores; therefore, it is an excellent candidate algorithm for providing solutions on this domain given its capabilities to seamlessly handle categorical and missing data, which is frequently a feature in educational datasets.

Customized Rule-Based Model to Identify At-Risk Students and Propose Rational Remedial Actions

Detecting at-risk students provides advanced benefits for improving student retention rates, effective enrollment management, alumni engagement, targeted marketing improvement, and institutional effectiveness advancement. One of the success factors of educational institutes is based on accurate and timely identification and prioritization of the students requiring assistance. The main objective of this paper is to detect at-risk students as early as possible in order to take appropriate correction measures taking into consideration the most important and influential attributes in students’ data. This paper emphasizes the use of a customized rule-based system (RBS) to identify and visualize at-risk students in early stages throughout the course delivery using the Risk Flag (RF). Moreover, it can serve as a warning tool for instructors to identify those students that may struggle to grasp learning outcomes. The module allows the instructor to have a dashboard that graphically depicts the students’ performance in different coursework components. The at-risk student will be distinguished (flagged), and remedial actions will be communicated to the student, instructor, and stakeholders. The system suggests remedial actions based on the severity of the case and the time the student is flagged. It is expected to improve students’ achievement and success, and it could also have positive impacts on under-performing students, educators, and academic institutions in general.

An active human role is essential in big data-led decisions and data-intensive science

Big data is transforming many sectors, with far-reaching consequences to how decisions are made and how knowledge is produced and shared. In the current move toward more data-led decisions and data-intensive science, we aim here to examine three issues that are changing the way data are read and used. First, there is a shift toward paradigms that involve a large amount of data. In such paradigms, the creation of complex data-led models becomes tractable and appealing to generate predictions and explanations. This necessitates for instance a rethinking of Occam's razor principle in the context of knowledge discovery. Second, there is a growing erosion of the human role in decision making and knowledge discovery processes. Human users' involvement is decreasing at an alarming rate, with no say on how to read, process, and summarize data. This makes legal responsibility and accountability hard to define. Third, thanks to its increasing popularity, big data is gaining a seductive allure, where volume and complexity of big data can de facto confer more persuasion and significance to knowledge or decisions that result from big-data-based processes. These issues call for an active human role by creating opportunities to incorporate, in the most unbiased way, human expertise and prior knowledge in decision making and knowledge production. This also requires putting in place robust monitoring and appraisal mechanisms to ensure that relevant data is answering the right questions. As the proliferation of data continues to grow, we need to rethink the way we interact with data to serve human needs.

Is There a Real Need for the Preparatory Years in Higher Education? An Educational Data Analysis for College and Future Career Readiness

Universities seek to qualify students for their academic and career futures and meet labor market requirements. Hence, a preparatory year is provided to bridge the gap between high school outcomes and the needs of university study plans. The preparatory year is the first year of support in the life of university students, and for decades, it has been recognized as important. It is considered the most crucial stage in the life of university students, where they build and refine their skills and choose their academic major, in which they complete their academic and career life. Due to the importance of this year, which requires the full attention and care of the higher authorities in terms of preparation, development, and renewal, this research outlines the importance of the preparatory year at a local level and in international institutions. Moreover, it sheds light on the details of King Abdulaziz University (KAU) students as a case study. It measures the relationship between the admission weighted ratio (AWR), the college enrollment allocation weighted ratio (CEAWR), and the performance of three batches of male and female students (three consecutive years), with details of students’ college allocation after the end of the preparatory year. More importantly, it aims to realize students’ progress through their weighted averages during their preparatory year, and the extent to which the goals of the preparatory year are achieved. After an analytic survey of the reality of the preparatory year, based on the statistical tests conducted, this study found that it is not possible to be satisfied with the weighted ratio for colleges’ direct allocation of high school students. The tests showed a difference between the AWR and that of the CEAWR, which indicates a change in the level of students’ performance from high school to university, due to the positive impact of the preparatory year. More precisely, it was noted that there is a possibility of studying the sufficiency of the weighted ratio for the direct allocation of some colleges in future research.

A Systematic Literature Review of Student’ Performance Prediction Using Machine Learning Techniques

Educational Data Mining plays a critical role in advancing the learning environment by contributing state-of-the-art methods, techniques, and applications. The recent development provides valuable tools for understanding the student learning environment by exploring and utilizing educational data using machine learning and data mining techniques. Modern academic institutions operate in a highly competitive and complex environment. Analyzing performance, providing high-quality education, strategies for evaluating the students’ performance, and future actions are among the prevailing challenges universities face. Student intervention plans must be implemented in these universities to overcome problems experienced by the students during their studies. In this systematic review, the relevant EDM literature related to identifying student dropouts and students at risk from 2009 to 2021 is reviewed. The review results indicated that various Machine Learning (ML) techniques are used to understand and overcome the underlying challenges; predicting students at risk and students drop out prediction. Moreover, most studies use two types of datasets: data from student colleges/university databases and online learning platforms. ML methods were confirmed to play essential roles in predicting students at risk and dropout rates, thus improving the students’ performance.

Schoolchildren’ Depression and Anxiety Risk Factors Assessment and Prediction: Machine Learning Techniques Performance Analysis (Preprint)

Background

Depression and anxiety symptoms in early childhood have a major effect on children’s mental health growth and cognitive development. The effect of mental health problems on cognitive development has been studied by researchers for the last 2 decades.

Objective

In this paper, we sought to use machine learning techniques to predict the risk factors associated with schoolchildren’s depression and anxiety.

Methods

The study sample consisted of 3984 students in fifth to ninth grades, aged 10-15 years, studying at public and refugee schools in the West Bank. The data were collected using the health behaviors schoolchildren questionnaire in the 2013-2014 academic year and analyzed using machine learning to predict the risk factors associated with student mental health symptoms. We used 5 machine learning techniques (random forest [RF], neural network, decision tree, support vector machine [SVM], and naive Bayes) for prediction.

Results

The results indicated that the SVM and RF models had the highest accuracy levels for depression (SVM: 92.5%; RF: 76.4%) and anxiety (SVM: 92.4%; RF: 78.6%). Thus, the SVM and RF models had the best performance in classifying and predicting the students’ depression and anxiety. The results showed that school violence and bullying, home violence, academic performance, and family income were the most important factors affecting the depression and anxiety scales.

Conclusions

Overall, machine learning proved to be an efficient tool for identifying and predicting the associated factors that influence student depression and anxiety. The machine learning techniques seem to be a good model for predicting abnormal depression and anxiety symptoms among schoolchildren, so the deployment of machine learning within the school information systems might facilitate the development of health prevention and intervention programs that will enhance students’ mental health and cognitive development.

Analysis of Emergency Remote Education in COVID-19 Crisis Focused on the Perception of the Teachers

This descriptive study intends to identify the satisfaction perception among the teachers of the Universidad del Valle de México (UVM) concerning the use of the Microsoft Teams platform in the transition from traditional model (face-to-face) to 100% online education [Emergency Remote Teaching (ERT)]. The proposal aims to determine the perspectives of teachers regarding the use of the Microsoft Teams platform during the crisis caused by COVID-19. UVM has 6938 full-time teachers and part-time teachers who collaborated in educational programs during January-June 2020 in the 33 campuses of UVM. And an instrument was developed and applied using finite population sampling, UVM perspective of teachers, which was distributed via Google Forms. The feasibility of the data collection instrument was determined by the Cronbach’s Alpha coefficient, with a result of 0.926. The data collection period was aligned with the first isolation period: 23 March to 20 April. The results in the perception of teacher satisfaction in the different sections of the instrument established an agreement in the answers (very satisfied or satisfied) regarding values that were higher than 60% in terms of satisfaction using the equipment. The analysis of the data collected was performed to verify the proposed hypothesis with the R version 4.0 software. A G-test was performed with the Logverosimilitude coefficient to test whether the categorical variables were independent (qualitative variables that are not defined continuously). The Krammer coefficient of association was then calculated to measure the correlation.