Division X Butuan Visit 2026 Core Group Online Meeting last September 26, 2026

 

Key Outcomes

Planning is underway for a September 30 visit to Butuan combining three activities: a demo meeting at Father Saturnino Urios University (FSU/Oreo) at 1:00 PM, a meeting with the LGU/City Mayor's Office at approximately 3:30 PM, and an installation of officers in the evening at 6:30 PM. 1 Role assignments for the demo meeting are partially identified but not yet finalized. Attendance confirmation from Downtown Butuan Toastmasters remains the critical blocker, particularly regarding President Hemadi and VP Gilbert. 23

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Decisions Made

• Demo meeting format: Follow the Digos program template (district-issued); roles include Toastmaster of the Day, Grammarian, Ah-Counter/Timer, Table Topics, Prepared Speech, and General Evaluator. 45
• Direk Bobby to deliver the prepared speech (same speech used in Digos); confirmed willing to repeat it if no other speaker is available. 67
• Installation venue: Leaning toward Balangay Hotel (where the club holds regular meetings); Amara also considered as an alternative for overnight stay. 89
• Travel plan: Depart Davao by 5:00 AM on September 30 to arrive in Butuan by approximately 10:00–11:00 AM. 8
• Installation deadline: Must be completed by end of September per district directive (Suzette's guidance). 10
• Presentation materials: Use Direk Bobby's short, straightforward Tagum template rather than the lengthy Rainer PowerPoint; fewer words preferred. 1112

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Role Assignments (Demo Meeting — Partial)

• Call to Order / Speech: Direk Bobby 6
• General Evaluator: Tm Jed (if available from Butuan) 13
• Evaluator of Speech: VP Gilbert (confirmed willing) 14
• Toastmaster of the Day: Ephrelyn (tentative) 13
• Grammarian / Ah-Counter / Timer: Jen (dual role if needed) 15
• Table Topics: Preferably assigned to a Downtown Butuan member 16

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Installation Program Flow

• Discharging of outgoing officers → Induction of new members → Acceptance speech → Valedictorian address 1718
• Keynote speaker: Sir Jed proposed; Direk Bobby to confirm his availability. 1920
• If President Ched is absent, Vice President to deliver the valedictory speech. 17
• Roasting/tribute segment (Table Topics style) before valedictory address is optional and can be removed. 21
• Program estimated to start at 6:30 PM. 22

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Blockers & Risks

• Downtown Butuan attendance unconfirmed: President Hemadi unresponsive; Gilbert is the primary point of contact but has limited availability. 223
• Ma'am Jojo and Ma'am Ched are out of town and cannot confirm attendance for September 30. 1524
• Low headcount risk: Demo meeting requires sufficient numbers to make a credible impression on FSU; showing up with only 3–4 people is not acceptable. 2526
• Wednesday is a workday, limiting availability of LGU employees and school contacts for the demo. 27
• Caraga State University contact (Rex Paro) not responding; university has exam week, reducing student availability. 28

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Pending Confirmation

• Sir Jed's availability to fly in from Butuan as keynote speaker. 19
• Final headcount from Downtown Butuan (target: at least 4 members). 29
• Mayor's schedule confirmation by Monday (Ma'am Crevi to follow up with Gem). 30
• FSU/Oreo permit: names of all attendees must be submitted in advance. 26
• Hotel booking: Direk Bobby to check Amara via friend; Ephrelyn to ask Tm Addi (Inland manager) about a discount. 31
• Charter fee status for Oreo club: PHP 7,000 confirmed as within budget; individual membership fees are separate. 32

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Action Items

• Jen: Convert demo meeting program to editable Word/Google Docs format; share via Google Drive for role input by members. 2933
• Jen: Send attendee name list to FSU/Oreo by Monday for permit processing. 26
• Jen: Request soft copy of materials and flyer from PRM Charity for distribution at the demo. 33
• Jen / Ephrelyn: Follow up with Downtown Butuan members (Gilbert, Hemadi) to confirm participation by Monday. 26
• Direk Bobby: Text Hemadi directly — ask if installation is pushing through for both Butuan and Downtown clubs. 334
• Direk Bobby: Contact Sir Jed to confirm keynote availability for installation. 19
• Direk Bobby: Check Amara hotel rates via personal contact; share hotel details once confirmed. 9
• Ma'am Crevi: Message Sir Jed to confirm keynote; get mayor's schedule confirmation by Monday. 1830
• Ephrelyn: Individually call/confirm role-takers for demo meeting by Monday night. 35

Session 7 Lay Counselling Training

 

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

 

Note: This is an unlisted video, only those with link can view. 

This is not available for public viewing.

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.