Decision making is a cognitive process that involves selecting a course of action or choice from among multiple alternatives. It's a fundamental aspect of human life and is present in various contexts, ranging from everyday situations to complex professional, personal, and strategic scenarios, such as resource allocations, risk management, strategic planning, cybersecurity, supply chain management, Web information systems (e.g., information retrieval and recommender systems), and so forth. Factors influencing decision making may include cognitive biases (mental shortcuts that can lead to errors in judgment), emotions, cultural and social influences, personal experiences, time constraints, and the availability of information.

Optimization, on the other hand, is a systematic process that aims to find the best possible solution based on specific criteria. It involves mathematical and computational techniques to minimize or maximize an objective function while adhering to a set of constraints. Optimization seeks to identify the globally optimal solution, which is the best solution achievable according to the defined criteria. Multiple optimization techniques have been proposed and applied in machine learning and AI, such as convex optimization, non-linear optimization, evolutionary algorithms, multi-objective optimization, game theroies, etc.

Both decision making and optimization have been widely applied in multiple domains, including business strategies, supply chains, educational optimization, etc. There are several scenarios in education where decision making and optimization play an important role, such as resource allocations (e.g., budget, time, personnel, infrastructure distribution and allocations, etc.), curriculum development (e.g., learning-path optimization, topics and materials recommendations, etc.), scheduling and planning (e.g., class, exam or activity scheduling, etc.), educational assessment and testing (e.g., auto-scoring, multi-objective assessment, etc.).

The primary objective of this half-day workshop is to facilitate the integration of decision-making and optimization principles within the field of educational research. We specifically welcome submissions that leverage decision-making and optimization methodologies to enhance learning analytics, improve educational optimization, and advance educational development and technologies.

Topics of Interest
We encourage any submissions which utilize decision making and/or optimization methodologies to assist learning analytics and educational technologies. The relevant topics include but are not limited to:

  • Decision Making and Optimization Methods
    • Group Decision Making and Negotiation Analysis
    • Multi-Criteria Decision Making
    • Multi-Objective Decision Making and Optimization
    • Linear/Non-Linear Optimization
    • Evolutionary Algorithms
    • Applied Optimization Technologies
    • Human factor-based Decision Making, e.g., personality, trust, emotional analysis
    • Visualization and Interface Design to Assist Decision Making and Optimization

  • Educational Scenarios and Applications (with the methods above)
    • Learning Analytics
    • Educational Data Mining
    • Education Assessment
    • Scheduling and Planning
    • Data-Informed Decision Making in Education
    • Personalized and Adaptive Learning
    • Educational Recommender Systems

Submission Instructions
Authors should prepare their manuscripts by using ACM two-column format, as shown below.

We accept full and short paper submissions through a single-blind reviewing process. The full paper is limited to 8 pages (including references) by using ACM two-column format, where the short paper is limited to 4 pages (including references). The accepted submissions will be archived in CEUR Workshop Proceedings. Please submit your paper via EasyChair System.

Important Dates
  • Submission Deadline: Dec 5, 2023 Dec 16, 2023
  • Notification of Acceptance: January 13, 2024
  • Workshop Dates: March 18, 2024