First International iOBE Workshop: Technological University of the Philippines

On Friday, 9 October 2026, I had the honour of conducting an online seminar-workshop, Introduction and Application of the iOBE Software for the Assessment of Learning Outcomes, for the Technological University of the Philippines (TUP), Manila. This was the first official iOBE workshop delivered to a university outside Malaysia, and a meaningful milestone for The Integrated OBE Software (iOBE).

The workshop was organised by the College of Industrial Education (CIE) and brought together close to 60 faculty members from colleges across TUP Manila.

The heart of the session was an extended, live demonstration of iOBE in action. Working through a sample course from start to finish, I showed how a course’s assessment data and outcome mappings are prepared and run through the software. I then took participants carefully through each output: the graphs of individual student marks, the box plots of marks for each assessment and outcome, and the population charts showing how students are distributed across mark bands. Together, these outputs reveal how student assessment results translate into measurable attainment of course and program outcomes, at a level of detail that grades alone cannot provide.

The demonstration then moved to what proved to be a real game changer: using AI to analyse iOBE results. Typical outcomes reports summarise each outcome with only three to five data points, such as the average mark and the percentages of students passing or failing. iOBE, by contrast, produces 14 data points for every course or program outcome. Its box plots give the full statistical profile of marks, including outliers, and its population charts show the share of students in each of six mark bands alongside the outcome’s weightage. This richness is what makes AI analysis so powerful. Working from iOBE’s outputs, AI examined each outcome in depth, uncovered patterns that the eye alone would miss, and recommended targeted improvements for both students and the course. It then brought these findings together into a complete course-level report, showing participants how outcomes data can move quickly from analysis to evidence-based continuous quality improvement.

The discussion reflected the strong commitment of TUP faculty to outcomes-based education. It also showed a shared interest in moving from documenting outcomes to measuring and using them, and that is exactly where iOBE aims to help.

I would like to express my sincere thanks to Dr. Apollo P. Portez, Dean of the College of Industrial Education, for the kind invitation and support. I also thank Dr. Neil Andrew F. Calayag, Head of the Professional Industrial Education Department and Project Leader of the activity, who coordinated the workshop programme and liased with me throughout the preparations. My thanks also go to the TUP faculty members who joined and participated in the workshop.

I look forward to continuing this collaboration with TUP and supporting more institutions in strengthening their outcomes assessment.

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3rd iOBE Workshop @ UPNM Engineering

The third iOBE workshop for the Faculty of Engineering, Universiti Pertahanan Nasional Malaysia (UPNM) was conducted online on 17 September 2026. This half-day session, titled "Strengthening the iOBE Implementation" builds on the introductory seminar held in March 2025 and the full-day hands-on workshop in May 2025.

A key focus this time was a deeper exploration of the foundational mathematical formulations underlying the iOBE software — a topic the participants had been keen to understand further since the previous sessions. The workshop walked through how assessment marks are converted into course and programme outcomes through the AT-CO and AT-PO mapping matrices, and how the weightage and normalisation mechanisms work across different mapping styles.

The participants then moved into the hands-on segment, with successful implementation of the iOBE software in processing several data at the course and multiple-course levels, generating outcomes reports with box plots and population charts ready for use in their Course Assessment Reports.

A major highlight of this workshop was the introduction of an AI Analyst developed specifically to work with iOBE output data. The AI Analyst interprets and provides deep insights on course and student performance by triangulating across the different graphs and statistical outputs that iOBE produces. This represents a significant step forward in how iOBE data can be used — moving beyond visual inspection of charts towards AI-assisted analysis that can identify patterns, flag areas of concern, and suggest targeted improvements for CQI at both the course and programme levels.

Many thanks to the Dean of the Faculty of Engineering, PM Dr Mohd Taufik, for the invitation, and to the Deputy Dean of Academic, PM Dr Ku Zarina, Mrs Hapsa, and Mrs Santy for coordinating and facilitating the workshop. This third session with FK UPNM marks a maturing partnership, with the faculty now well-positioned to integrate iOBE and its AI capabilities into their assessment and accreditation workflows.

This workshop marks the second contribution to the Malaysian tertiary academic community by the Centre for Academic Excellence (ÆXcel) of i-CATS UC.

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iOBE Seminar at the University of Nottingham Malaysia

I had the pleasure of conducting an online workshop this morning titled "iOBE: Introduction, Usage and Benefits" for the Faculty of Science and Engineering (FOSE) at the University of Nottingham Malaysia, representing the Centre for Academic Excellence (ÆXcel) at i-CATS University College, Kuching, Sarawak.

I would like to thank Assoc. Prof. Bee Yean Low, Chair of the FOSE Accreditation and Quality Assurance Working Group and Programme Director of BPharm (Hons) at the School of Pharmacy, for the kind invitation and for coordinating the session.

The workshop was structured in two parts. The first hour focused on the conceptual foundations — the context and challenges of implementing Outcome-Based Education (OBE), how student outcomes are typically evaluated, the limitations of conventional approaches, and how iOBE addresses these challenges through compact, detailed outcomes visuals such as box plots and population charts. I also shared the mathematical framework behind the software, covering how assessment marks are converted into course outcomes and programme outcomes through flexible mapping.

The second hour was practical. I demonstrated iOBE on real course data, showing how the software computes and visualises outcomes at both the course level and the programme level. Participants saw how iOBE generates assessment-level outcomes, course outcomes with box plots and population charts, programme outcomes aggregated from multiple courses, and iCGPA spiderweb charts for individual students. I also demonstrated how iOBE can be used to assess specific skills such as problem-solving through the 3-Step Approach.

The participants came from a wide range of backgrounds and experience levels. Many were encountering iOBE for the first time, while a smaller group had some prior awareness of the software. I was pleasantly surprised to learn that one of the lecturers at the faculty had previously used iOBE for her programme at another university and had introduced it to her colleagues at UNM. It was gratifying to hear of this firsthand experience, and it reinforced the value of the software being adopted across different institutions.

It was encouraging to introduce iOBE to a new audience and to see the interest in how it can support their OBE assessment and accreditation needs.

Thank you once again to Assoc. Prof. Bee Yean Low and the Faculty of Science and Engineering at UNM for the opportunity.

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Presenting iOBE at the REES & I-PHEX 2025

From September 30 to October 1, 2025, the REES & I-PHEX 2025 conference was held in Johor Bahru, Malaysia, hosted by UTM’s Centre for Engineering Education (CEE). I presented a paper here on “Implementing iOBE for Visualizing and Interpreting Student Outcomes: A Course-Level Assessment”, demonstrating the use of the iOBE software across multiple cohorts of a third-year engineering course.

The presentation showcased how iOBE was applied to analyze student outcome data across three consecutive cohorts in an advanced engineering course. By aggregating diverse assessment tools mapped to Programme Outcomes, the software generated box plots and population graphs that revealed consistent patterns — stronger performance in collaborative tasks and recurring gaps in individual problem-solving. This multi-year analysis highlighted meaningful trends and supported targeted, data-informed improvements in teaching and learning.

This work was also presented as a poster exhibition at the I-PHEX Expo titled "From Complexity to Clarity: Transforming Outcome-Based Assessment through iOBE", recognized with a Silver Award. The poster highlighted key aspects of the software, particularly its advantages, significance, impact, and potential for all educational levels.

With Prof. Homero Murzi of Texas A&M University (Chairman of the Research in Engineering Education Network).

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Sharing iOBE Data Safely to Protect Data Privacy & Student Identity

The iOBE system generates valuable and insightful data analytics into student performance, offering deeper insights to educators, students, and institutions alike.

While these benefits are clear, the way in which iOBE data is shared is equally important. All sharing of the iOBE outputs in this website strives to follow relevant data privacy standards to protect the identity of each student.

In this post, I will outline the principles and practices I follow when sharing iOBE data, providing context, examples, and illustrations of how these data are presented and shared safely and effectively.

1. Data is Presented Only in Aggregated and Collective Form

All iOBE outputs that I share online or publish formally are aggregated summaries of assessment results. These are typically presented as Box Plots or Population Charts (as shown in the figure below) that represent the overall performance of a class or cohort.

Key characteristics of this approach:

• No row-level data is released. That is, no dataset is published where each row corresponds to a specific student’s marks across assessment elements.
• Individual scores are never displayed directly; instead, they are merged into collective statistics that summarise group trends.
• No personal identifiers (names, IDs, email addresses, or any other identifiable attributes) are ever included in the published outputs.

This method ensures that while the general performance of the group is clearly communicated, no single student’s results can be isolated or traced back to them.

2. Use of Simulated or Adapted Datasets When Necessary

Where possible, iOBE visuals are generated from simulated or adapted datasets — particularly in demonstration, training, or public outreach contexts.

When real data is used, the following safeguards apply when I want to share those data online:

• Removal of all identifiers before any analysis or visualisation takes place.
• Presentation in aggregated visual form only, never as individual rows or lists.
• Exclusion of small group data where small sample sizes could increase the likelihood of indirect identification.

In certain cases, real datasets may undergo minor controlled modifications (e.g., small randomised adjustments to scores) to further reduce the risk of re-identification (to ensure the privacy of students' data), while keeping overall trends intact.

3. Compliance with Legal and Ethical Data Protection Standards

The iOBE data-sharing process has been designed to align with:

• Best practices in educational data protection, as recommended in academic and institutional guidelines.

Specific compliance measures include:

• Anonymisation by aggregation – Once scores are summarised in statistical form, they no longer constitute personal data under PDPA definitions, provided they cannot be used to identify an individual.
• Removal of identifiers – No names, student IDs, or indirect identifiers are included in shared outputs.
• No re-identification risk – Outputs are constructed in a way that prevents linking back to an individual, even if additional external information is available.

This compliance framework ensures that published visuals are not only useful but also legally sound and ethically responsible.

4. Advantages of Aggregated Visual Sharing Over Row-Based Lists

It is still common practice in some settings to share results in row-based lists—often anonymised using passcodes or partial student IDs. While this may appear to protect privacy, it has several drawbacks:

• Re-identification risk remains – Students can often deduce each other’s codes, especially in small cohorts or close-knit groups.
• Limited insight – Row lists only show individual scores without revealing the broader performance trends.
• Increased complexity for interpretation – Students must scan through multiple rows to guess where they stand compared to peers.

By contrast, iOBE visual outputs:

• Provide instant performance context through statistical summaries.
• Eliminate direct identification risk by avoiding row-level disclosures entirely.
• Communicate trends, variability, and benchmarks more efficiently than lists.

In short, iOBE’s method not only meets privacy standards but also delivers richer and more actionable insights.

5. Purpose and Benefits of the iOBE Data-Sharing Approach

The aim of sharing iOBE data is twofold:

• Educational value – To provide clear and meaningful feedback to students and educators, enabling targeted improvement.
• Trust and transparency – To demonstrate that data is handled with care, in line with legal and ethical obligations.

For educators, these visuals:

• Highlight class-wide strengths and weaknesses.
• Support evidence-based teaching decisions.
• Serve as a communication tool that is both privacy-conscious and information-rich.

For students, aggregated visuals help them:

• Understand their position in relation to class medians, quartiles, and performance ranges.
• Recognise areas where improvement is needed, without the discomfort of being individually exposed.

6. Summary of Data Privacy Safeguards

To reiterate, all iOBE data sharing follows these principles:

• Aggregated data only – No individual records are published.
• No student identifiers – Neither direct nor indirect identifiers are included.
• Effective anonymisation – Data is presented in a form that prevents re-identification.

Final Note

The iOBE data-sharing framework reflects a commitment to maximising educational benefit while minimising privacy risks. By replacing traditional row-based result lists with aggregated visual summaries, educators can share important performance insights in a format that is clear and secure.

For further details on implementing this approach, please feel free to contact me here.

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