DepEd conducts master classes to strengthen teacher collaboration

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The Department of Education (DepEd) has launched the Master Classes for Kindergarten and Grades 1, 4, and 7 (K147) Teachers Across All Learning Areas to enhance teacher collaboration and support the implementation of the Revised K-12 Curriculum.

The five-day master class, held from November 25 to 29 during the In-Service Training (INSET), encourages educators to work together, share knowledge, and continuously improve outcomes through shared leadership and collective learning.

“Ito po ay isang inisiyatibo ng Department of Education, ‘yong pagbibigay po sa inyo ng capacity development and professional development through our master classes para kayo ay ma-capacitate sa inyong laban sa araw-araw,” Undersecretary and Chief of Staff Fatima Lipp Panontongan said.

“Hindi po biro ang inyong pinagdadaanan every day. ‘Pag may kalamidad, kayo po ang first responders natin. Kayo po ang tumatayong magulang ng ating learners sa ating mga eskwelahan,” she added.

The master classes are being livestreamed through the official Facebook pages of host regions and division offices, reaching over 50,000 teachers nationwide. During the sessions, education experts and practitioners conduct trainings on subject matter and teaching techniques to further enhance teachers’ knowledge, skills, and capacities.

The CPD-accredited master classes complement the revised curriculum and provide teachers with the tools needed for its effective implementation. DepEd, through the National Educators Academy of the Philippines, in collaboration with BCD and BLD have identified experts from HEIs, private and public universities to provide educators with comprehensive topics. Likewise, select regional and division offices have become their partners in broadcasting each session through Facebook live.

“These master classes will serve as a testament to your unwavering commitment to strengthening yourselves as pillars of education. By immersing yourselves in this lofty pursuit, you are not only enhancing your skills but also deepening your capacity to guide, protect, and inspire your students,” National Educators Academy of the Philippines (NEAP) Director Jennifer Lopez said.

DepEd, partners report positive educational outcomes with phone-based tutoring program

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The Department of Education (DepEd) has observed significant development and improvements in learners’ numeracy skills through a phone-based tutoring program.

In partnership with Innovations for Poverty Action (IPA) and Youth Impact, DepEd is implementing the mEducation program, an evidence-based targeted education intervention that uses weekly messages and phone calls to deliver targeted math instructions to Grades 3 to 5 learners.

“The mEducation program embraces technology that addresses the challenges in learning recovery and remediation strategies for learners with specific learning needs in mathematics. The phone-based tutoring approach makes learning more accessible, personal, and collaborative,” DepEd MIMAROPA Regional Director Dr. Nicolas Capulong said.

From April to July 2024, 1,015 learners and 265 teachers participated in the program's first round of implementation across 90 schools in nine School Division Offices (SDOs). Results indicate that the 20-minute tutoring sessions over an eight-week period improved academic performance.

The percentage of learners unable to identify basic operations has been reduced to 15 percent from 56 percent. Meanwhile, the percentage of those able to solve division problems has increased from 2 percent to 17 percent.

In addition, mEducation increased student confidence and class participation, encouraged active parental involvement during phone tutoring sessions, and contributed to higher job satisfaction for teachers.

“The mEducation program has significantly helped improve learners’ numeracy skills and strengthened the home-school partnership, encouraging parents to plan an active role in their children’s learning,” Zamboanga Sibugay Schools Division Superintendent Virgilio Batan, Jr. said.

IPA, alongside DepEd and Youth Impact, is committed to expanding the reach of mEducation in other regions, including the Ministry of Basic, Higher, and Technical Education (MBHTE) in the Bangsamoro Autonomous Region in Muslim Mindanao (BARMM).



Likewise, DepEd will continue to implement this scalable after-school learning program to support learners needing remedial education.

Ateneo develops AI to forecast market interest rates

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Mathematicians from the Ateneo de Manila University have developed artificial intelligence (AI) deep learning tools that can help predict money market interest rates, invaluable for decision-makers in business and government.



Governments and businesses need AI in order to make sense of, and stay afloat in, a world inundated with data. SOURCE: Luca Sammarco / Pexels.com


In simple terms, the market interest rate is the cost of borrowing money or the reward for saving it. This changes based on supply and demand: if many people are borrowing but few are saving, rates go up; if the opposite happens, rates go down. Interest rates are also affected by inflation (since higher prices mean higher rates) as well as by countries’ central banks (which adjust rates to help the economy grow or to control rising prices). Essentially, interest rates help determine how money moves in the economy.


“Interest rates are among the most important macroeconomic factors considered by both government and private entities when making investment and policy decisions. A reliable forecast is a requisite to sound management of exposure to different types of risk,” the researchers explained.


The Ateneo researchers tested two deep learning models: Multi-layer Perceptrons (MLP) and Vanilla Generative Adversarial Networks (VGAN). Both successfully anticipated changes in Philippine Benchmark Valuation (BVAL) rates before and during the pandemic, showcasing the models’ robust capability to potentially foresee economic fluctuations and market disruptions.


MLP is a type of artificial neural network that passes the data through a series of cells, each of which processes the information in its own way and adds to the network’s overall understanding of the data. This method is often used for image recognition and machine translation because of its ability to find complex patterns in data. Meanwhile, VGAN actually consists of two networks: a generator that creates synthetic data, and a discriminator that evaluates the data’s authenticity. By working in opposition to each other—hence, “adversarial”—the networks are able to refine and improve their analyses.



Conceptual diagrams of artificial intelligence learning models, MLP (top) and VGAN (bottom). SOURCE: Bata et al.


The researchers found that both models produced reliable forecasts of one-, three-, six-month, and one-year BVAL rates within the limits of the datasets used. They successfully predicted key trends by incorporating as many as 16 domestic and global economic indicators, including inflation, exchange rates, and credit default swaps. MLP showed strong performance with fewer variables and simpler structures, making it an efficient choice for straightforward analyses. Meanwhile, VGAN excelled more in analyzing complex scenarios, achieving high accuracy when working with larger datasets.


The practical implications of these AI deep learning models are substantial, according to the researchers: financial institutions could potentially deploy them to manage market, credit, liquidity, and other risks; and governments could also potentially use these models to optimize debt issuance strategies by reducing borrowing costs.


The study highlights the growing role of AI in financial decision-making and suggests exploring more advanced neural network designs to further enhance forecasting accuracy. It is hoped that businesses and policymakers will come to embrace these technologies in order to gain a competitive advantage in a rapidly evolving data-driven landscape.


Halle Megan L. Bata, Mark Jayson A. Victoria, Wyonna Chezska B. Alvarez, Elvira P. de Lara-Tuprio, and Armin Paul D. Allado published their paper, “Deep Learning Approaches in Interest Rate Forecasting,” last November 15 in the journal, AIP Conference Proceedings.



SOURCE: https://archium.ateneo.edu/mathematics-faculty-pubs/288/