University of Southeastern Philippines and Universitas Brawijaya Economics Collaborative Online International Learning (E-COIL) Program Session 1

 

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Key Outcomes

The University of Southeastern Philippines (USeP) and Universitas Brawijaya (UB) Indonesia officially launched a Collaborative Online International Learning (COIL) program 1. Students from both institutions will collaborate on a comparative economic analysis of the Philippines and Indonesia, focusing on international trade, monetary economics, and regional integration within ASEAN.

Project Objectives & Scope

• Academic Goals: Enhance international economic understanding, analytical competencies, and intercultural collaboration
• Core Activity: Students will compare macroeconomic indicators and policies between the two nations to identify similarities and differences
• Curriculum Integration: The program aims to internationalize the BS Economics program, targeting a level four accreditation and alignment with the Asian University Network
• Cultural Exchange: Foster friendships and professional networking between Filipino and Indonesian students

Deliverables & Requirements

• Final Outputs: Each group must produce a comparative analysis paper and a corresponding PowerPoint presentation .
• Presentation Structure:
  • Introduction and economic background .
  • Methodology based on published data from statistics authorities and central banks .
  • Comparative results and interpretation of economic structures .
  • Conclusion focusing on policy implications for both countries.
• Collaboration Method: Students will use WhatsApp as the primary communication tool for group coordination.

Program Logistics

• Timeline: The program spans approximately 10 weeks, with a final culmination and presentation planned for November
• Session Format: A mix of synchronous sessions (approximately 40%) and asynchronous work (over 50%)
• Group Composition: 26 groups have been formed, balancing students from USeP (MFE 411 and DE 314 classes) and UB
• Topic Selection: A list of 26 tentative integrated topics is provided, though groups have the freedom to propose their own as long as they avoid duplication

Action Items

• Professor Jennifer: Prepare a Google Sheet for groups to submit and lock in their chosen topics to avoid duplication
• Group Leaders: Establish WhatsApp chat groups and coordinate the delegation of data gathering (Indonesian students gather UB data; Filipino students gather USeP data)
• Students: Use the provided templates for accomplishment and progress reports to document collaboration

Online Faculty Orientation on Special Needs Education: Understanding Common Disabilities and Classroom Accommodations

 

ARISE International Lecture Series on Big Data and Time Series Models by Prof. Yoonsuk Lee of Kangwon National University last September 22, 2026

 

Key Outcomes

Professor Yoonsuk Lee provided a comprehensive lecture on the evolution of agricultural big data in South Korea, the distinction between machine learning and traditional econometrics, and the practical application of time series models like ARIMA for agricultural forecasting 123.

Agricultural Big Data Evolution in Korea

The transition of agricultural data in South Korea occurred in four distinct stages:

• Stage 1 (1990s–early 2000s): Conversion of paper records to digital databases and computerized administrative work 4.
• Stage 2: Introduction of RFID and ubiquitous sensor networks for continuous monitoring and traceability (e.g., beef supply chains) 5.
• Stage 3 (2013–2019): Expansion of smartphone technology and the rise of "Smart Farms," particularly in Gangwon province for crops like cabbage and paprika 67.
• Stage 4: Current focus on digital transformation and AI innovation to integrate fragmented data across production, distribution, and consumption 8.

Big Data Characteristics and Challenges

• The 3 Vs: Big data is defined by Volume (scale), Velocity (speed of arrival), and Variety (different formats) 9.
• Biological Variability: Unlike industrial data, agricultural data is uniquely challenged by biological variability, where the same inputs can produce different outputs due to soil, weather, and growth stages 1011.
• Data Quality: AI cannot fix "poor data"; errors in raw data collection (e.g., incorrect labor records) will persist in the AI output 12.

Modeling Approaches: Machine Learning vs. Econometrics

• Machine Learning (ML): Effective for large datasets with complex, non-linear relationships and high dimensionality 2.
• Econometrics: Preferred for causal identification, precise explanation, and interpretable parameters 13.
• Supervised Learning: Uses labeled data to map inputs to known outputs (e.g., price prediction or disease classification) 14.
• Unsupervised Learning: Finds hidden patterns or groups without pre-defined labels (e.g., clustering farmers by characteristics to tailor government policy) 1516.
• Selection Criteria: Traditional econometric models are recommended over ML if the dataset is small (e.g., only 30 observations) because ML requires splitting data into training and testing sets, further reducing the available learning data 1718.

Time Series Analysis and ARIMA

• Core Principle: The order of observations is critical; randomly reorganizing time series data destroys essential information 1920.
• Stationarity: A requirement for many models where the statistical behavior remains stable over time. Non-stationary data is typically handled through differencing or log transformations 212223.
• ARIMA Model: A basic univariate model consisting of Autoregressive (AR), Integrated (I), and Moving Average (MA) components 2425.
• Outlier Modeling: Mention of the "outlier model" to catch structure breaks in data, a topic further explored in Professor Jennifer's research 2627.

Practical Guidance for Students

• Missing Data: For small amounts of missing data, an average of nearby variables (e.g., 3-year or 5-year average) can be used, provided the result is compared against the original regression to ensure similarity 2829.
• Workflow: Students are advised to plot data visually first to identify trends and seasonality before applying AI or complex models 2330.
• Consultation: Students are encouraged to consult their professors rather than relying solely on AI for model selection 31.