A Deep Learning Neural Network Proposal Experiment for Predicting and Improving School Results

Prof. Nilza Y. Cruz and Irving A. Jiménez

Overview:

This proposal suggests conducting an experiment that uses a Deep Learning Neural Network to predict and improve school results. The goal is to leverage advanced machine learning algorithms to accurately predict student performance and improve it through personalized interventions. This will help to identify students who are at risk of falling behind and provide them with the necessary support to succeed. The experiment will involve collecting and analyzing large amounts of data to develop accurate models that can predict student outcomes.

Case in Point:

Traditional methods for identifying and predicting students who need differentiated learning or instruction [1], as well as the?resources needed by teachers and administrators to achieve that goal, often rely on reactive measures, such as:

• Historical test scores
• Isolated observations of competency gaps by instructional standards [2]
• Ad-hoc efforts
• Current grades

These linear approaches may not provide a comprehensive view of a student's future learning needs for academic performance.?The linearity fails to capture the full picture of a student's required skills and abilities. Neither teachers nor school administrators are provided with a clear and systematic roadmap to make sure that the school attains its academic performance objectives.

The goal of the proposal experiment is not just to build a predictive model, but to use the insights from the model to improve student success and overall school outcomes.

Plan of Action:

To address the shortfalls, we can leverage Artificial Intelligence (AI) techniques, including Machine Learning Models (MLM) and Neural Networks. These AI-driven predictive models are capable of simultaneously analyzing a multitude of variables, thereby furnishing a comprehensive map of each student's academic journey and their academic limitations.

By implementing these predictive models, we can identify potential learning obstacles earlier and intervene proactively, discontinuing the student's path-dependent educational trajectory [3]. This path may, to some degree, be attributed to the specific instructional context to which the student has been exposed throughout their educational experience, among other factors.?

The proposed model cannot use general-purpose neural networks like GPT (Generative Pre-trained Transformer) due to the unique circumstances of the student's learning journey, influenced by the institution's teaching approach, and cultural, operational, and resource settings. This type of network is?designed to handle a wide range of tasks and topics, leveraging its large size and broad training data.?In contrast, neural networks optimized for specific applications are better suited for particular school contexts.

A properly designed neural network could predict students' academic performance, graduation rate, and state test achievement in reading, algebra, and geometry, as well as identify the resources needed for differentiated instruction. This data-driven approach aims to enable proactive interventions and targeted support, ultimately fostering student and school success [4], [5], and [6].

Why an AI Neural Network?

Neural networks are a type of machine-learning model inspired by the human brain's neural structure. The interconnected nodes in these networks process and transmit information via a series of weighted connections (links), each of which reflects the strength of the connection between inputs. Each layer in the network feeds its inputs down to the next layer, creating increasingly abstract representations. In this manner, it mimics the mechanism through which biological neurons function.?

The strength of neural networks lies in their ability to learn through a technique called backpropagation, which allows them to adjust the weights (adjustable parameters within the neural network) continuously from data. Moreover, the model identifies intricate patterns and connections that may not immediately be apparent, resulting in a layered and multidimensional landscape that has been inaccessible to educators and supervisors [7, 8].

Proposed Architecture

The envisioned neural network architecture comprises several layers of neurons (input, hidden, and output), each responsible for extracting and processing features from the input data to get an output.

The input layer neurons will incorporate a comprehensive set of student information, including:

• demographics,
• academic records,
• attendance patterns,
• socioeconomic factors,
• and any other relevant variables.

Subsequently, multiple hidden layers of neurons (a stack of boxes) will perform computations [9], combining and weighting the input features (represented by labels) [10] to uncover complex patterns and relationships that contribute to student performance. These hidden layers will act as the “knobs” of the neural network, adjusting the influence of various inputs and allowing the model to learn and adapt during the training process. For the output layer, we plan to design it to consist of four units, which will provide the predicted probabilities for each of the following outcomes:

1. Passing the state test in reading

2. Passing the state test in algebra

3. Passing the state test in geometry

4. Graduating at the end of the academic year

Training and Evaluation:

To train the neural network, the work involves leveraging historical student data, including, among other things, actual outcomes (passing or failing state tests and graduation rates). This labeled data will serve as ground validation for the model to learn from, allowing it to adjust its internal parameters, or weights, to minimize prediction errors.

The performance of the trained model will be rigorously evaluated using various metrics, such as accuracy, precision, recall, and F1-score [11]. Additionally, the model's predictions will be compared to existing benchmarks and conventional predictive methods to assess its effectiveness.

Example

Consider whether a student will pass a test based on their homework grades.

• The homework grades are the?inputs.
• The “Pass/Fail” status is the?label.
• The?weights?determine how important each homework grade is for predicting the outcome.

The network continuously updates the weights until it finds a combination that accurately predicts the labels.

Ethical Considerations:

While the potential of this predictive model is appealing, it is imperative to recognize the importance of addressing ethical concerns. Student data will be handled with the utmost confidentiality and security, and measures will be taken to mitigate potential biases in the model's predictions. Transparency will be maintained by clearly explaining the model's outputs to educators, administrators, and parents, fostering trust and understanding.

Conclusion?

The limitations of conventional methods for identifying and assisting students who require differentiated learning, as well as the research-based strategies that educators can employ to fill the gaps, underscore the necessity for a more comprehensive and data-driven approach. With artificial intelligence and neural networks, we can make models that give a complete picture of each student's academic journey, potential obstacles, and the best ways to deal with them.?

The proposed neural network architecture, designed with subject-specific optimization rather than general-purpose models, can analyze a multitude of student-level variables to predict outcomes such as state test performance and graduation rates. This proactive, data-driven approach enables early identification of at-risk students, allowing for timely interventions and targeted support to disrupt potential path-dependent trajectories.?

The neural network's capacity to discern intricate patterns and connections that are otherwise unobservable can provide educators and administrators with a systematic plan to attain the educational objectives of the institution.

By incorporating ethical considerations and maintaining transparency, these predictive models can be responsibly implemented to foster student success and overall school improvement.?

Through the integration of AI-powered predictive analytics, schools can transition from reactive, linear approaches to a more comprehensive, data-driven framework for supporting student learning and outcomes. This innovative solution has the potential to transform the way we identify and address the diverse needs of students, ultimately paving the way for enhanced educational outcomes and greater student success.

References and Notes:

1. “Differentiated Instruction (DI) is fundamentally the attempt to teach differently to different students, rather than maintain a one-size-fits-all approach to instruction.” (Differentiated Instruction | Center for Teaching and Learning (stanford.edu)).
2. “School instruction standards are the learning goals and expectations set by each state that guide what students should know and be able to do, while curriculum and teaching methods are determined locally to help students achieve those standards.” (https://www.pcsb.org/domain/11775 and https://www.findlaw.com/education/curriculum-standards-school-funding/national-school-curriculum-standards.html).
3. “The concept of path dependence highlights how a student's learning journey can become locked into certain trajectories due to the cumulative effects of early experiences, institutional factors, and self-reinforcing behaviors.” (https://www.investopedia.com/terms/p/path-dependency.asp, and?Path dependence — Wikipedia).?
4. Baniata LH, Kang S, Alsharaiah MA, Baniata MH. Advanced Deep Learning Model for Predicting the Academic Performances of Students in Educational Institutions. Applied Sciences. 2024; 14(5):1963. https://doi.org/10.3390/app14051963).
5. Li, X., Zhang, Y., Cheng, H. et al. Student achievement prediction using deep neural network from multi-source campus data. Complex Intell. Syst.?8, 5143–5156 (2022). https://doi.org/10.1007/s40747-022-00731-8 (https://link.springer.com/article/10.1007/s40747-022-00731-8#citeas).
6. Chavez, H., María, J., & Raymundo, C. (2023). Artificial neural network model to predict student performance using nonpersonal information. Frontiers in Education, 8, 1106679. https://doi.org/10.3389/feduc.2023.1106679 (https://www.frontiersin.org/articles/10.3389/feduc.2023.1106679/full)
7. From: Suleyman, M., & Bhaskar, M. (2023). The coming wave: Technology, power, and the twenty-first century's greatest dilemma. Crown. (Page 59)
8. Nielsen, M. (2015). Neural networks and deep learning. Determination Press. (https://www.ise.ncsu.edu/fuzzy-neural/wp-content/uploads/sites/9/2022/08/neuralnetworksanddeeplearning.pdf). (Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.).
9. We're assessing how many layers and how many neurons each layer requires for a particular school in the experiment.
10. Labels: the target values we will use to predict. In our student outcome prediction model, labels would be things like “Pass” or “Fail” for state test predictions. “Graduates” or “Does not graduate” for graduation predictions.
11. F1 is a popular evaluation metric for classification tasks. In supervised learning, classification is a type of task in which the model is trained to predict the category or class of an input, with the goal of assessing its effectiveness. (F1 Score in Machine Learning - GeeksforGeeks) and (02. PyTorch Neural Network Classification - Zero to Mastery Learn PyTorch for Deep Learning).

Final Remarks

The proposal is a compilation of the knowledge we have learned from references, notes, and inputs that we have gathered from various authors, academics, practitioners, and personal professional experiences. Also, utilizing AI platforms as a research assistant to conserve time and to check for the structural logical coherence of expressions. This proposal suggests conducting an experiment that uses a Deep Learning Neural Network to predict and improve school results. The goal is to leverage advanced machine learning algorithms to accurately predict student performance and improve it through personalized interventions. This will help to identify students who are at risk of falling behind and provide them with the necessary support to succeed. The experiment will involve collecting and analyzing large amounts of data to develop accurate models that can predict student outcomes.