A Comprehensive Survey on Deep Learning Techniques in Educational Data Mining

Educational Data Mining (EDM) has emerged as a vital field of research, which harnesses the power of computational techniques to analyze educational data. With the increasing complexity and diversity of educational data, Deep Learning techniques have shown significant advantages in addressing the challenges associated with analyzing and modeling this data. This survey aims to systematically review the state-of-the-art in EDM with Deep Learning. We begin by providing a brief introduction to EDM and Deep Learning, highlighting their relevance in the context of modern education. Next, we present a detailed review of Deep Learning techniques applied in four typical educational scenarios, including knowledge tracing, undesirable student detecting, performance prediction, and personalized recommendation. Furthermore, a comprehensive overview of public datasets and processing tools for EDM is provided. Finally, we point out emerging trends and future directions in this research area.Comment: 21 pages, 5 figure

Tukey Regressive Hoover Indexed Deep Shift-Invariant Neural Network for Student Behavior Prediction

Prediction of student performance in the academic field creates significant challenges in developing reliable and accurate diagnosis models. Through the use of online learning behavior data, this paper may assist teachers in identifying students with learning challenges in advance and providing timely assistance. A novel technique called Tukey Regressive Hoover indexed Deep Shift Invariant Structure Neural Network (TRHIDSISNN) Model is introduced for student behaviour analysis with lesser time consumption. Initially, the student data and features are collected and transmitted to the input layer. After that, the features of collected student data are analyzed in hidden layer 1 with help of the Tukey Regression. The correlation between one or more independent features is identified to find the dependent feature. The relevant features are sent to the hidden layer 2. In that layer, the Hoover index is applied for analyzing the training and testing features. Finally, the hidden layer result is sent to the output layer where the hyperbolic tangent activation function is used to classify the data that belongs to that particular class. Based on the classification, the student grade level is predicted as high, medium and low based on their behavior gets displayed. Experimental assessment is carried out using different parameters such as prediction accuracy, false-positive rate, prediction time, and space complexity with respect to the number of student data.  The discussed results show that when compared to state-of-the-art approaches, the suggested TRHIDSISNN model achieves higher accuracy with shorter prediction times

Computational Intelligence for the Micro Learning

The developments of the Web technology and the mobile devices have blurred the time and space boundaries of people’s daily activities, which enable people to work, entertain, and learn through the mobile device at almost anytime and anywhere. Together with the life-long learning requirement, such technology developments give birth to a new learning style, micro learning. Micro learning aims to effectively utilise learners’ fragmented spare time and carry out personalised learning activities. However, the massive volume of users and the online learning resources force the micro learning system deployed in the context of enormous and ubiquitous data. Hence, manually managing the online resources or user information by traditional methods are no longer feasible. How to utilise computational intelligence based solutions to automatically managing and process different types of massive information is the biggest research challenge for realising the micro learning service. As a result, to facilitate the micro learning service in the big data era efficiently, we need an intelligent system to manage the online learning resources and carry out different analysis tasks. To this end, an intelligent micro learning system is designed in this thesis. The design of this system is based on the service logic of the micro learning service. The micro learning system consists of three intelligent modules: learning material pre-processing module, learning resource delivery module and the intelligent assistant module. The pre-processing module interprets the content of the raw online learning resources and extracts key information from each resource. The pre-processing step makes the online resources ready to be used by other intelligent components of the system. The learning resources delivery module aims to recommend personalised learning resources to the target user base on his/her implicit and explicit user profiles. The goal of the intelligent assistant module is to provide some evaluation or assessment services (such as student dropout rate prediction and final grade prediction) to the educational resource providers or instructors. The educational resource providers can further refine or modify the learning materials based on these assessment results

Fine Grain Synthetic Educational Data: Challenges and Limitations of Collaborative Learning Analytics

While data privacy is a key aspect of Learning Analytics, it often creates difficulty when promoting research into underexplored contexts as it limits data sharing. To overcome this problem, the generation of synthetic data has been proposed and discussed within the LA community. However, there has been little work that has explored the use of synthetic data in real-world situations. This research examines the effectiveness of using synthetic data for training academic performance prediction models, and the challenges and limitations of using the proposed data sharing method. To evaluate the effectiveness of the method, we generate synthetic data from a private dataset, and distribute it to the participants of a data challenge to train prediction models. Participants submitted their models as docker containers for evaluation and ranking on holdout synthetic data. A post-hoc analysis was conducted on the top 10 participant’s models by comparing the evaluation of their performance on synthetic and private validation datasets. Several models trained on synthetic data were found to perform significantly poorer when applied to the non-synthetic private dataset. The main contribution of this research is to understand the challenges and limitations of applying predictive models trained on synthetic data in real-world situations. Due to these challenges, the paper recommends model designs that can inform future successful adoption of synthetic data in real-world educational data systems

Evaluating the Effectiveness of Human-Centered AI Systems in Education

Trabajo Fin de Máster. Máster Universitario en sistemas inteligentes. Curso académico 2022-2023.[ES]Esta tesis explora el uso de la inteligencia artificial (IA) en la educación, centrándose en mejorar la interacción humano-computadora (HCI) y la experiencia del usuario. El estudio incluye una revisión sis temática de la literatura (SLR) y un estudio de caso del proyecto LATILL, un ejemplo destacado del uso de IA en la educación. La SLR examina el conjunto de literatura existente para determinar los efectos de la integración de la IA en la educación en la experiencia del usuario y en la HCI. Los resultados demuestran cómo la IA puede personalizar y adaptar las experiencias de aprendizaje, mejorar el rendimiento en tareas y mejorar la experiencia del usuario tanto para los docentes como para los estudiantes. La SLR también iden tifica las dificultades y restricciones relacionadas con la aplicación de la IA en la educación. El proyecto LATILL, que ejemplifica el uso efectivo de la IA en la educación de idiomas, es el foco del estudio de caso. El objetivo principal del proyecto es ayudar a los docentes a proporcionar orientación y apoyo personaliza dos a sus estudiantes. Pueden seleccionar textos apropiados según los niveles del Marco Común Europeo de Referencia para las Lenguas (CEFR) y características lingüísticas. La plataforma utiliza una metodología de diseño centrada en el usuario e incorpora prototipos y comentarios de los usuarios para garantizar una funcionalidad óptima y satisfacer los requisitos particulares de los docentes. El proyecto LATILL busca transformar la enseñanza de idiomas convencional, aumentar la participación de los estudiantes y fomentar experiencias de aprendizaje de idiomas gratificantes y exitosas mediante la promoción de la colaboración y el intercambio de recursos entre los educadores. A través de la SLR y el estudio de caso, esta tesis propor ciona conocimientos valiosos sobre el potencial de la IA en la educación, su impacto en la experiencia del usuario y la HCI, y los desafíos y oportunidades que surgen al implementar la IA en entornos educativos. En conclusión, esta investigación resalta los beneficios significativos de la integración de la IA en la educación y enfatiza la importancia de considerar los principios de experiencia del usuario y HCI al diseñar sistemas educativos impulsados por la IA. Al aprovechar de manera efectiva las tecnologías de IA y adoptar enfoques de diseño centrados en el usuario, los educadores pueden mejorar la experiencia de aprendizaje, fomentar la participación de los estudiantes y promover resultados educativos exitosos.[EN]This thesis explores using artificial intelligence (AI) in education, concentrating on improving human computer interaction (HCI) and the user experience. The study includes a systematic review of the literature (SLR) and a case study of the LATILL project, a prime example of the use of AI in education. The SLR examines the body of existing literature to determine the effects of integrating AI in education on user experience and HCI. The results demonstrate how AI can personalize and adapt learning experiences, en hance task performance, and improve user experience for teachers and students. The SLR also identifies difficulties and restrictions related to the application of AI in education. The LATILL project, which ex emplifies the effective use of AI in language education, is the focus of the case study. The project’s main objective is to assist teachers in providing their students with individualized guidance and support. They can select appropriate texts based on CEFR levels and linguistic characteristics. The platform employs a user-centered design methodology and incorporates prototypes and user feedback to guarantee optimal functionality and satisfy the particular requirements of teachers. The LATILL project seeks to transform conventional language instruction, increase student engagement, and foster enjoyable and successful lan guage learning experiences by encouraging collaboration and resource sharing among educators. Through the SLR and the case study, this thesis provides valuable insights into the potential of AI in education, its impact on user experience and HCI, and the challenges and opportunities that arise in implementing AI in educational settings. In conclusion, this research highlights the significant benefits of integrating AI in education and emphasizes the importance of considering user experience and HCI principles when design ing AI-driven educational systems. By leveraging AI technologies effectively and adopting user-centered design approaches, educators can enhance the learning experience, promote student engagement, and foster successful educational outcomes

Computational Methods for Medical and Cyber Security

Over the past decade, computational methods, including machine learning (ML) and deep learning (DL), have been exponentially growing in their development of solutions in various domains, especially medicine, cybersecurity, finance, and education. While these applications of machine learning algorithms have been proven beneficial in various fields, many shortcomings have also been highlighted, such as the lack of benchmark datasets, the inability to learn from small datasets, the cost of architecture, adversarial attacks, and imbalanced datasets. On the other hand, new and emerging algorithms, such as deep learning, one-shot learning, continuous learning, and generative adversarial networks, have successfully solved various tasks in these fields. Therefore, applying these new methods to life-critical missions is crucial, as is measuring these less-traditional algorithms' success when used in these fields

The role of machine learning in identifying students at-risk and minimizing failure

Education is very important for students' future success. The performance of students can be supported by the extra assignments and projects given by the instructors for students with low performance. However, a major problem is that students at-risk cannot be identified early. This situation is being investigated by various researchers using Machine Learning techniques. Machine learning is used in a variety of areas and has also begun to be used to identify students at-risk early and to provide support by instructors. This research paper discusses the performance results found using Machine learning algorithms to identify at-risk students and minimize student failure. The main purpose of this project is to create a hybrid model using the ensemble stacking method and to predict at-risk students using this model. We used machine learning algorithms such as Naive Bayes, Random Forest, Decision Tree, K-Nearest Neighbors, Support Vector Machine, AdaBoost Classifier and Logistic Regression in this project. The performance of each machine learning algorithm presented in the project was measured with various metrics. Thus, the hybrid model by combining algorithms that give the best prediction results is presented in this study. The data set containing the demographic and academic information of the students was used to train and test the model. In addition, a web application developed for the effective use of the hybrid model and for obtaining prediction results is presented in the report. In the proposed method, it has been realized that stratified k-fold cross validation and hyperparameter optimization techniques increased the performance of the models. The hybrid ensemble model was tested with a combination of two different datasets to understand the importance of the data features. In first combination, the accuracy of the hybrid model was obtained as 94.8% by using both demographic and academic data. In the second combination, when only academic data was used, the accuracy of the hybrid model increased to 98.4%. This study focuses on predicting the performance of at-risk students early. Thus, teachers will be able to provide extra assistance to students with low performance

Personalized face and gesture analysis using hierarchical neural networks

The video-based computational analyses of human face and gesture signals encompass a myriad of challenging research problems involving computer vision, machine learning and human computer interaction. In this thesis, we focus on the following challenges: a) the classification of hand and body gestures along with the temporal localization of their occurrence in a continuous stream, b) the recognition of facial expressivity levels in people with Parkinson's Disease using multimodal feature representations, c) the prediction of student learning outcomes in intelligent tutoring systems using affect signals, and d) the personalization of machine learning models, which can adapt to subject and group-specific nuances in facial and gestural behavior. Specifically, we first conduct a quantitative comparison of two approaches to the problem of segmenting and classifying gestures on two benchmark gesture datasets: a method that simultaneously segments and classifies gestures versus a cascaded method that performs the tasks sequentially. Second, we introduce a framework that computationally predicts an accurate score for facial expressivity and validate it on a dataset of interview videos of people with Parkinson's disease. Third, based on a unique dataset of videos of students interacting with MathSpring, an intelligent tutoring system, collected by our collaborative research team, we build models to predict learning outcomes from their facial affect signals. Finally, we propose a novel solution to a relatively unexplored area in automatic face and gesture analysis research: personalization of models to individuals and groups. We develop hierarchical Bayesian neural networks to overcome the challenges posed by group or subject-specific variations in face and gesture signals. We successfully validate our formulation on the problems of personalized subject-specific gesture classification, context-specific facial expressivity recognition and student-specific learning outcome prediction. We demonstrate the flexibility of our hierarchical framework by validating the utility of both fully connected and recurrent neural architectures

Education Research Using Data Mining and Machine Learning with Computer Science Undergraduates

In recent decades, we are witness to an explosion of technology use and integration of everyday life. The engine of technology application in every aspect of life is Computer Science (CS). Appropriate CS education to fulfill the demand from the workforce for graduates is a broad and challenging problem facing many universities. Research into this ‘supply–chain’ problem is a central focus of CS education research. As of late, Educational Data Mining (EDM) emerges as an area connecting CS education research with the goal to help students stay in their program, improve performance in their program, and graduate with a degree. We contribute to this work with several research studies and future work focusing on CS undergraduate students relating to their program success and course performance analyzed through the lens of data mining. We perform research into student success predictors beyond diversity and gender. We examine student behaviors in course load and completion. We study workforce readiness with creation of a new teaching strategy, its deployment in the classroom, and the analysis shows us relevant Software Engineering (SE) topics for computing jobs. We look at cognitive learning in the beginning CS course its relations to course performance. We use decision trees in machine learning algorithms to predict student success or failure of CS core courses using performance and semester span of core curriculum. These research areas refine pathways for CS course sequencing to improve retention, reduce time-to–graduation, and increase success in the work field