Relationship between Cooperative learning method and Students’ Mathematics Learning Achievement: A Meta-Analysis Correlation

An appropriate learning method can improve students’ learning achievement. The cooperative learning method encourages students to improve their mathematics learning achievement. Mathematics learning achievement is one indicator of achieving learning objectives. Mathematics is an important lesson to be learned and must be mastered by students. The purpose of this research was to analyze the effect size of the relationship between Cooperative learning method and mathematics learning achievement through meta-analysis quantitative research approach. In this research, the cooperative learning method serves as an independent variable, and mathematics learning achievement serves as the dependent variable. The data was obtained from the online database search results on Google Scholar in 2010-2020. The sample used was 16 research publications that have met the specified criteria. The data analysis technique used was the meta-analysis quantitative approach with correlation meta-analysis. The results showed that there was a positive and significant relationship (level of 5%) between the cooperative learning method and students’ mathematics learning achievement. The average effect size value was 0.15 in the range of 0.04 to 0.27 which was included in the low category. These results have proven the consistency of the findings of previous research.

Decision Support Systems for Risk Assessment in Credit Operations Against Collateral

With the global economic crisis, which reached its peak in the second half of 2008, and before a market shaken by economic instability, financial institutions have taken steps to protect the banks’ default risks, which had an impact directly in the form of analysis in credit institutions to individuals and to corporate entities. To mitigate the risk of banks in credit operations, most banks use a graded scale of customer risk, which determines the provision that banks must do according to the default risk levels in each credit transaction. The credit analysis involves the ability to make a credit decision inside a scenario of uncertainty and constant changes and incomplete transformations. This ability depends on the capacity to logically analyze situations, often complex and reach a clear conclusion, practical and practicable to implement. Credit Scoring models are used to predict the probability of a customer proposing to credit to become in default at any given time, based on his personal and financial information that may influence the ability of the client to pay the debt. This estimated probability, called the score, is an estimate of the risk of default of a customer in a given period. This increased concern has been in no small part caused by the weaknesses of existing risk management techniques that have been revealed by the recent financial crisis and the growing demand for consumer credit.The constant change affects several banking sections because it prevents the ability to investigate the data that is produced and stored in computers that are too often dependent on manual techniques. Among the many alternatives used in the world to balance this risk, the provision of guarantees stands out of guarantees in the formalization of credit agreements. In theory, the collateral does not ensure the credit return, as it is not computed as payment of the obligation within the project. There is also the fact that it will only be successful if triggered, which involves the legal area of the banking institution. The truth is, collateral is a mitigating element of credit risk. Collaterals are divided into two types, an individual guarantee (sponsor) and the asset guarantee (fiduciary). Both aim to increase security in credit operations, as an payment alternative to the holder of credit provided to the lender, if possible, unable to meet its obligations on time. For the creditor, it generates liquidity security from the receiving operation. The measurement of credit recoverability is a system that evaluates the efficiency of the collateral invested return mechanism. In an attempt to identify the sufficiency of collateral in credit operations, this thesis presents an assessment of smart classifiers that uses contextual information to assess whether collaterals provide for the recovery of credit granted in the decision-making process before the credit transaction become insolvent. The results observed when compared with other approaches in the literature and the comparative analysis of the most relevant artificial intelligence solutions, considering the classifiers that use guarantees as a parameter to calculate the risk contribute to the advance of the state of the art advance, increasing the commitment to the financial institutions.Com a crise econômica global, que atingiu seu auge no segundo semestre de 2008, e diante de um mercado abalado pela instabilidade econômica, as instituições financeiras tomaram medidas para proteger os riscos de inadimplência dos bancos, medidas que impactavam diretamente na forma de análise nas instituições de crédito para pessoas físicas e jurídicas. Para mitigar o risco dos bancos nas operações de crédito, a maioria destas instituições utiliza uma escala graduada de risco do cliente, que determina a provisão que os bancos devem fazer de acordo com os níveis de risco padrão em cada transação de crédito. A análise de crédito envolve a capacidade de tomar uma decisão de crédito dentro de um cenário de incerteza e mudanças constantes e transformações incompletas. Essa aptidão depende da capacidade de analisar situações lógicas, geralmente complexas e de chegar a uma conclusão clara, prática e praticável de implementar. Os modelos de Credit Score são usados para prever a probabilidade de um cliente propor crédito e tornar-se inadimplente a qualquer momento, com base em suas informações pessoais e financeiras que podem influenciar a capacidade do cliente de pagar a dívida. Essa probabilidade estimada, denominada pontuação, é uma estimativa do risco de inadimplência de um cliente em um determinado período. A mudança constante afeta várias seções bancárias, pois impede a capacidade de investigar os dados que são produzidos e armazenados em computadores que frequentemente dependem de técnicas manuais. Entre as inúmeras alternativas utilizadas no mundo para equilibrar esse risco, destacase o aporte de garantias na formalização dos contratos de crédito. Em tese, a garantia não “garante” o retorno do crédito, já que não é computada como pagamento da obrigação dentro do projeto. Tem-se ainda, o fato de que esta só terá algum êxito se acionada, o que envolve a área jurídica da instituição bancária. A verdade é que, a garantia é um elemento mitigador do risco de crédito. As garantias são divididas em dois tipos, uma garantia individual (patrocinadora) e a garantia do ativo (fiduciário). Ambos visam aumentar a segurança nas operações de crédito, como uma alternativa de pagamento ao titular do crédito fornecido ao credor, se possível, não puder cumprir suas obrigações no prazo. Para o credor, gera segurança de liquidez a partir da operação de recebimento. A mensuração da recuperabilidade do crédito é uma sistemática que avalia a eficiência do mecanismo de retorno do capital investido em garantias. Para tentar identificar a suficiência das garantias nas operações de crédito, esta tese apresenta uma avaliação dos classificadores inteligentes que utiliza informações contextuais para avaliar se as garantias permitem prever a recuperação de crédito concedido no processo de tomada de decisão antes que a operação de crédito entre em default. Os resultados observados quando comparados com outras abordagens existentes na literatura e a análise comparativa das soluções de inteligência artificial mais relevantes, mostram que os classificadores que usam garantias como parâmetro para calcular o risco contribuem para o avanço do estado da arte, aumentando o comprometimento com as instituições financeiras

Data Balancing Techniques for Predicting Student Dropout Using Machine Learning

This research article was published MDPI, 2023Predicting student dropout is a challenging problem in the education sector. This is due to an imbalance in student dropout data, mainly because the number of registered students is always higher than the number of dropout students. Developing a model without taking the data imbalance issue into account may lead to an ungeneralized model. In this study, different data balancing techniques were applied to improve prediction accuracy in the minority class while maintaining a satisfactory overall classification performance. Random Over Sampling, Random Under Sampling, Synthetic Minority Over Sampling, SMOTE with Edited Nearest Neighbor and SMOTE with Tomek links were tested, along with three popular classification models: Logistic Regression, Random Forest, and Multi-Layer Perceptron. Publicly accessible datasets from Tanzania and India were used to evaluate the effectiveness of balancing techniques and prediction models. The results indicate that SMOTE with Edited Nearest Neighbor achieved the best classification performance on the 10-fold holdout sample. Furthermore, Logistic Regression correctly classified the largest number of dropout students (57348 for the Uwezo dataset and 13430 for the India dataset) using the confusion matrix as the evaluation matrix. The applications of these models allow for the precise prediction of at-risk students and the reduction of dropout rates

The Effectiveness of Hybrid Learning in Improving of Teacher-Student Relationship in Terms of Learning Motivation

The Advanced Mathematical Thinking (AMT) ability is one of the prioritized mathematical abilities needed to be developed in learning mathematics during tertiary education. The present study sought to test the effectiveness of hybrid learning in improving students' advanced mathematical thinking. The research used a quasi-experimental design with a non-equivalent control group design. The subject of this study was students of a mathematics education study program at a university in Bandung who attended lecture for the multi-variable in a calculus course. The sampling technique used was purposive sampling. Of the many variable calculus classes consisting of 2 classes, one class was chosen as the experiment group and the other class as the control group. The sample consists of 40 people for each group. Data analysis used the MANOVA test with normality and homogeneity tests as a prerequisite test. The results showed a difference in AMT's significance between the hybrid learning and conventional groups, where hybrid learning had a higher AMT. Other than that, there is a difference in the significance of AMT between the high motivation group and the low motivation group, where high motivation has a higher AMT, and there is an interaction of learning models and motivational factors to increase AMT. Doi: 10.28991/esj-2021-01288 Full Text: PD

2018 Faculty Excellence Showcase, AFIT Graduate School of Engineering & Management

Excerpt: As an academic institution, we strive to meet and exceed the expectations for graduate programs and laud our values and contributions to the academic community. At the same time, we must recognize, appreciate, and promote the unique non-academic values and accomplishments that our faculty team brings to the national defense, which is a priority of the Federal Government. In this respect, through our diverse and multi-faceted contributions, our faculty, as a whole, excel, not only along the metrics of civilian academic expectations, but also along the metrics of military requirements, and national priorities

Predictive models as early warning systems for student academic performance in introductory programming

Computer programming is fundamental to Computer Science and IT curricula. At the novice level it covers programming concepts that are essential for subsequent advanced programming courses. However, introductory programming courses are among the most challenging courses for novices and high failure and attrition rates continue even as computer science education has seen improvements in pedagogy. Consequently, the quest to identify factors that affect student learning and academic performance in introductory computer programming courses has been a long-standing activity. Specifically, weak novice learners of programming need to be identified and assisted early in the semester in order to alleviate any potential risk of failing or withdrawing from their course. Hence, it is essential to identify at-risk programming students early, in order to plan (early) interventions. The goal of this thesis was to develop a validated, predictive model(s) with suitable predictors of student academic performance in introductory programming courses. The proposed model utilises the Naïve Bayes classification machine learning algorithm to analyse student performance data, based on the principle of parsimony. Furthermore, an additional objective was to propose this validated predictive model as an early warning system (EWS), to predict at-risk students early in the semester and, in turn, to potentially inform instructors (and students) for early interventions. We obtained data from two introductory programming courses in our study to develop and test the predictive models. The models were built with student presage and in progress-data for which instructors may easily collect or access despite the nature of pedagogy of educational settings. In addition, our work analysed the predictability of selected data sources and looked for the combination of predictors, which yields the highest prediction accuracy to predict student academic performance. The prediction accuracies of the models were computed by using confusion matrix data including overall model prediction accuracy, prediction accuracy sensitivity and specificity, balanced accuracy and the area under the ROC curve (AUC) score for generalisation. On average, the models developed with formative assessment tasks, which were partially assisted by the instructor in the classroom, returned higher at-risk prediction accuracies than the models developed with take-home assessment task only as predictors. The unknown data test results of this study showed that it is possible to predict 83% of students that need support as early as Week 3 in a 12-week introductory programming course. The ensemble method-based results suggest that it is possible to improve overall at-risk prediction performance with low false positives and to incorporate this in early warning systems to identify students that need support, in order to provide early intervention before they reach critical stages (at-risk of failing). The proposed model(s) of this study were developed on the basis of the principle of parsimony as well as previous research findings, which accounted for variations in academic settings, such as academic environment, and student demography. The predictive model could potentially provide early warning indicators to facilitate early warning intervention strategies for at-risk students in programming that allow for early interventions. The main contribution of this thesis is a model that may be applied to other programming and non-programming courses, which have both continuous formative and a final exam summative assessment, to predict final student performance early in the semester.Ohjelmointi on informaatioteknologian ja tietojenkäsittelytieteen opinto-ohjelmien olennainen osa. Aloittelijatasolla opetus kattaa jatkokurssien kannalta keskeisiä ohjelmoinnin käsitteitä. Tästä huolimatta ohjelmoinnin peruskurssit ovat eräitä haasteellisimmista kursseista aloittelijoille. Korkea keskeyttämisprosentti ja opiskelijoiden asteittainen pois jättäytyminen ovat vieläkin tunnusomaisia piirteitä näille kursseille, vaikka ohjelmoinnin opetuksen pedagogiikka onkin kehittynyt. Näin ollen vaikuttavia syitä opiskelijoiden heikkoon suoriutumiseen on etsitty jo pitkään. Erityisesti heikot, aloittelevat ohjelmoijat tulisi tunnistaa mahdollisimman pian, jotta heille voitaisiin tarjota tukea ja pienentää opiskelijan riskiä epäonnistua kurssin läpäimisessä ja riskiä jättää kurssi kesken. Heikkojen opiskelijoiden tunnistaminen on tärkeää, jotta voidaan suunnitella aikainen väliintulo. Tämän väitöskirjatyön tarkoituksena oli kehittää todennettu, ennustava malli tai malleja sopivilla ennnustusfunktioilla koskien opiskelijan akateemista suoriutumista ohjelmoinnin peruskursseilla. Kehitetty malli käyttää koneoppivaa naiivia bayesilaista luokittelualgoritmia analysoimaan opiskelijoiden suoriutumisesta kertynyttä aineistoa. Lähestymistapa perustuu yksinkertaisimpien mahdollisten selittävien mallien periaatteeseen. Lisäksi, tavoitteena oli ehdottaa tätä validoitua ennustavaa mallia varhaiseksi varoitusjärjestelmäksi, jolla ennustetaan putoamisvaarassa olevat opiskelijat opintojakson alkuvaiheessa sekä informoidaan ohjaajia (ja opiskelijaa) aikaisen väliintulon tarpeellisuudesta. Keräsimme aineistoa kahdelta ohjelmoinnin peruskurssilta, jonka pohjalta ennustavaa mallia kehitettiin ja testattiin. Mallit on rakennettu opiskelijoiden ennakkotietojen ja kurssin kestäessä kerättyjen suoriutumistietojen perusteella, joita ohjaajat voivat helposti kerätä tai joihin he voivat päästä käsiksi oppilaitoksesta tai muusta ympäristöstä huolimatta. Lisäksi väitöskirjatyö analysoi valittujen datalähteiden ennustettavuutta ja sitä, mitkä mallien muuttujista ja niiden kombinaatioista tuottivat kannaltamme korkeimman ennustetarkkuuden opiskelijoiden akateemisessa suoriutumisessa. Mallien ennustusten tarkkuuksia laskettiin käyttämällä sekaannusmatriisia, josta saadaan laskettua ennusteen tarkkuus, ennusteen spesifisyys, sensitiivisyys, tasapainotettu tarkkuus sekä luokitteluvastekäyriä (receiver operating characteristics (ROC)) ja näiden luokitteluvastepinta-ala (area under curve (AUC)) Mallit, jotka kehitettiin formatiivisilla tehtävillä, ja joissa ohjaaja saattoi osittain auttaa luokkahuonetilanteessa, antoivat keskimäärin tarkemman ennustuksen putoamisvaarassa olevista opiskelijoista kuin mallit, joissa käytettiin kotiin vietäviä tehtäviä ainoina ennusteina. Tuntemattomalla testiaineistolla tehdyt mallinnukset osoittavat, että voimme tunnistaa jo 3. viikon kohdalla 83% niistä opiskelijoista, jotka tarvitsevat lisätukea 12 viikkoa kestävällä ohjelmoinnin kurssilla. Tulosten perusteella vaikuttaisi, että yhdistämällä metodeja voidaan saavuttaa parempi yleinen ennustettavuus putoamisvaarassa olevien opiskelijoiden suhteen pienemmällä määrällä väärin luokiteltuja epätositapauksia. Tulokset viittaavat myös siihen, että on mahdollista sisällyttää yhdistelmämalli varoitusjärjestelmiin, jotta voidaan tunnistaa avuntarpeessa olevia opiskelijoita ja tarjota täten varhaisessa vaiheessa tukea ennen kuin on liian myöhäistä. Tässä tutkimuksessa esitellyt mallit on kehitetty nojautuen yksinkertaisimman selittävän mallin periaatteeseen ja myös aiempiin tutkimustuloksiin, joissa huomioidaan erilaiset akateemiset ympäristöt ja opiskelijoiden tausta. Ennustava malli voi tarjota indikaattoreita, jotka voivat mahdollisesti toimia pohjana väliintulostrategioihin kurssilta putoamisvaarassa olevien opiskelijoiden tukemiseksi. Tämän tutkimuksen keskeisin anti on malli, jolla opiskelijoiden suoriutumista voidaan arvioida muilla ohjelmointia ja muita aihepiirejä käsittelevillä kursseilla, jotka sisältävät sekä jatkuvaa arviointia että loppukokeen. Malli ennustaisi näillä kursseilla lopullisen opiskelijan suoritustason opetusjakson alkuvaiheessa

Academic Year 2019-2020 Faculty Excellence Showcase, AFIT Graduate School of Engineering & Management

An excerpt from the Dean\u27s Message: There is no place like the Air Force Institute of Technology (AFIT). There is no academic group like AFIT’s Graduate School of Engineering and Management. Although we run an educational institution similar to many other institutions of higher learning, we are different and unique because of our defense-focused graduate-research-based academic programs. Our programs are designed to be relevant and responsive to national defense needs. Our programs are aligned with the prevailing priorities of the US Air Force and the US Department of Defense. Our faculty team has the requisite critical mass of service-tested faculty members. The unique composition of pure civilian faculty, military faculty, and service-retired civilian faculty makes AFIT truly unique, unlike any other academic institution anywhere

Statistical Data Modeling and Machine Learning with Applications

The modeling and processing of empirical data is one of the main subjects and goals of statistics. Nowadays, with the development of computer science, the extraction of useful and often hidden information and patterns from data sets of different volumes and complex data sets in warehouses has been added to these goals. New and powerful statistical techniques with machine learning (ML) and data mining paradigms have been developed. To one degree or another, all of these techniques and algorithms originate from a rigorous mathematical basis, including probability theory and mathematical statistics, operational research, mathematical analysis, numerical methods, etc. Popular ML methods, such as artificial neural networks (ANN), support vector machines (SVM), decision trees, random forest (RF), among others, have generated models that can be considered as straightforward applications of optimization theory and statistical estimation. The wide arsenal of classical statistical approaches combined with powerful ML techniques allows many challenging and practical problems to be solved. This Special Issue belongs to the section “Mathematics and Computer Science”. Its aim is to establish a brief collection of carefully selected papers presenting new and original methods, data analyses, case studies, comparative studies, and other research on the topic of statistical data modeling and ML as well as their applications. Particular attention is given, but is not limited, to theories and applications in diverse areas such as computer science, medicine, engineering, banking, education, sociology, economics, among others. The resulting palette of methods, algorithms, and applications for statistical modeling and ML presented in this Special Issue is expected to contribute to the further development of research in this area. We also believe that the new knowledge acquired here as well as the applied results are attractive and useful for young scientists, doctoral students, and researchers from various scientific specialties

Computational Optimizations for Machine Learning

The present book contains the 10 articles finally accepted for publication in the Special Issue “Computational Optimizations for Machine Learning” of the MDPI journal Mathematics, which cover a wide range of topics connected to the theory and applications of machine learning, neural networks and artificial intelligence. These topics include, among others, various types of machine learning classes, such as supervised, unsupervised and reinforcement learning, deep neural networks, convolutional neural networks, GANs, decision trees, linear regression, SVM, K-means clustering, Q-learning, temporal difference, deep adversarial networks and more. It is hoped that the book will be interesting and useful to those developing mathematical algorithms and applications in the domain of artificial intelligence and machine learning as well as for those having the appropriate mathematical background and willing to become familiar with recent advances of machine learning computational optimization mathematics, which has nowadays permeated into almost all sectors of human life and activity