The Ateneo Laboratory for the Learning Sciences is interested in quantitative analysis of student interactions with computer-based learning environments to derive new insights about how students learn best.

ALLS News and Events


Technology-Enhanced Learning Video Series Pilot Episode with Dr. Tanja Mitrovic

The pilot episode of Technology-Enhanced Learning features Dr. Tanja Mitrovic, a professor of Computer Science and Software Engineering at the University of Canterbury, New Zealand. Her research interests include intelligent tutoring systems, student modeling, and learning analytics.

In this episode, Dr. Didith Rodrigo invites Dr. Mitrovic to talk about her latest work on active video watching. Research shows that students learn from videos when they are completely engaged with the material. However, video-based learning tends to be a passive learning activity due to the absence of interaction with the video itself, the lack of interaction with other people, and the unavailability of feedback. To address this problem, Dr. Mitrovic together with Professor Vania Dimitrova from the University of Leeds (UK), developed AVW-Space, a platform that supports active video watching by providing note-taking, interactive visualizations, and personalized nudges.

To learn more about AVW-Space, watch the interview here.
Technology-Enhanced Learning is a joint production of the Ateneo Laboratory for the Learning Sciences and the Asia Pacific Society for Computers in Education.
1:22 What is active video watching?3:31 How does your project promote or support active video watching?
5:41 What are nudges and are they useful in promoting active learning?
11:04 AVW and softskills.
13:12 How do interested teachers use your Active Video Watching (AVW) platform?
14:14 If a teacher cannot use AVW, what are some lessons from your research that teachers can transfer to their classes?
16:00 Final words from Dr. Mitrovic (advice young researchers about how to develop or nurture their careers)

Technology-Enhanced Learning Video Series Trailer

Welcome to Technology-Enhanced Learning: How technology is changing the way we learn, a series that features brief summaries of scientific research and innovation in the area of computer-based learning.
In this series, respected researchers and academics from across the world will be invited to talk about their current projects, the impact these projects have on teachers and learners, and the questions that are still open for investigation.

We look forward to sharing insightful conversations with you!

Technology-Enhanced Learning is a joint production of the Ateneo Laboratory for the Learning Sciences (ALLS) and the Asia Pacific Society for Computers in Education (APSCE).

Are All Who Wander Lost? An Exploratory Analysis of Learner Traversals of Minecraft Worlds

The Ateneo Laboratory for the Learning Sciences cordially invites you to attend the talk titled “Are All Who Wander Lost? An Exploratory Analysis of Learner Traversals of Minecraft Worlds” by Maricel Esclamado on Tuesday, 05 April 2022 at 4:00 PM.

This talk is part of the ALLS Lecture Series. It will be held via Zoom. Please register at: https://go.ateneo.edu/ALLSLecture05042022

Admission is free.


We analyze in-game data and out-of-game assessment data from 15 Grade 6 boys from the Philippines who were completing a learning task with the What-If Hypothetical Implementations using Minecraft (WHIMC) to determine how distance traveled and area covered relate to assessment outcomes. We also determine the extent of overlap of areas covered by computing the Jaccard Index and Maximum Similarity Index (MSI). We find no significant correlation between assessment scores and overall distance, area, or MSI. However, when we break the data down into five-minute intervals, we find a significant negative correlation between assessment scores and distance traveled and the area covered during certain time periods. These findings suggest that wandering off early in gameplay may be indicative of low learning outcomes later on. The absence of a significant relationship between MSI and assessment scores suggests the absence of a canonical traversal in an open-ended environment.

About the Speaker:

Maricel A. Esclamado is a graduate student currently taking Ph.D. in Computer Science at Ateneo de Manila University. She has been working on the data analysis for the WHIMC project in Ateneo Laboratory for the Learning Sciences (ALLS) since September 2021. She is also an Associate Professor at the University of Science and Technology of Southern Philippines.

Call for Participants: Developing Learning Modules using What-If Hypothetical Implementations in Minecraft (WHIMC)


🚨 Calling STEM teachers from Grade 4 to Grade 10🚨

Join us for an online teacher training on Nurturing Interest in STEM among Filipino learners using Minecraft: Developing learning modules using What-If Hypothetical Implementations in Minecraft (WHIMC)!

📌This will be a 5-day online training composed of synchronous and asynchronous sessions where WHIMC will be explored, and you will be walked through the process of module development and designing classroom experience.

📌Teacher participants will have the opportunity to apply their learnings on module development. The expected output from the training course would be a class module aligned to the Philippine Basic Education curriculum.

📌 Teachers who will join just need their own laptop, stable internet connection, a mouse, a Zoom account and a Minecraft Java Edition account. ALLS may lend the teacher participants Minecraft Java Edition accounts, if needed.

📌Knowledge and experience with playing Minecraft is NOT required.

💻 Pre-register now through https://bit.ly/ALLSteachertraining

ALLS #44: Impact of Severe Weather Conditions on Online Learning during the COVID19 with Kiel Lagmay

The ALLS Youtube channel’s Season 3 finale features Mr. Kiel Lagmay, a Learning Management System (LMS) Analysis Research Assistant of the Ateneo Laboratory for Learning Sciences.

From October to November 2020, the Philippines was struck by eight typhoons, two of which caused widespread flooding, utilities interruptions, property destruction, and loss of life. How did these severe weather conditions affect online learning participation of undergraduate and graduate students in the midst of the COVID-19 pandemic?

In his talk entitled, “Quantifying the impact of Severe Weather Conditions on Online Learning During the COVID-19 Pandemic”, Mr. Lagmay discusses findings on the impact of the typhoons on student online participation as well as pertinent data analysis methods relevant to this context.

Eager to learn more? Click here.

Call for Papers: Computer-Based Learning in Context



Special Issue on The Long Tail of Algorithmic Bias in Education: Intersectionality and Less-Studied Categories of Identity


Call For Papers

The last couple of years have seen an explosion of interest in algorithmic bias in education, matching greater societal awareness of the problem of algorithmic bias in general, and problems of discrimination and social justice more broadly.

However, most of the work on algorithmic bias in education (and algorithmic bias in general) has focused on easily identified and well-known demographic categories. A recent review by Baker and Hawn (2021) finds that in the relatively rarer cases when researchers have looked for algorithmic bias in terms of other categories, they often find evidence for its existence. This suggests that other unknown categories may be impacting algorithmic effectiveness. Furthermore, algorithmic bias often is posed in terms of single categories, ignoring the possibility that bias may emerge at the intersection of categories as well.

This special issue seeks to promote research and practice that investigates and attempts to resolve less-studied algorithmic biases in education. Work on biases going beyond widely-studied demographic categories is welcome; this includes work that spans both widely-studied and non-widely-studied categories. Work on intersectional biases is also welcome. We welcome theoretical papers, conceptual and position papers, empirical papers, methodological papers, and papers of practice.

Sample Topics May Include:

  • Empirical research on whether algorithmic bias investigating less-studied categories is present in a specific application
  • Including but not limited to work involving indigenous populations, sub-categories of widely-studied demographic categories, learners with specific disabilities, neurodiversity, military-connected children, migrant workers and their families, non-binary and transgender learners, religious minorities, refugees, rural learners, learners in small or remote cities or communities, non-WEIRD countries, speakers of less common dialects or non-prestige dialects, second-language speakers, and international students or students of specific national backgrounds
  • Empirical research on intersectional algorithmic biases
  • Empirical work to address and resolve less-studied algorithmic bias and intersectional algorithmic biases
  • Mathematical work and methodology related to studying less-studied and/or intersectional algorithmic biases, including but not limited to power analyses and  sample size calculations
  • Conceptual, theoretical, and position pieces related to journal special issue themes
  • Work around data systems and methods that enable research on less-studied groups
  • Case studies around efforts to reduce algorithmic bias (of the type this special issue focuses on) in practice

Submission and Inquiries

Please see: https://www.upenn.edu/learninganalytics/CBLC/submission.html for submission information. We welcome manuscripts of any length and welcome dual-publication both in English and other languages.

When you submit your paper, please note that it is for this special
issue in your cover letter.
All submissions will go through the journal’s usual peer review process.


Important Dates:

  • Email inquiry of interest for submitting to special issues or abstract: Any date before August 1, 2022 (optional)
  • Paper submission: September 1, 2022
  • All articles will be published online as soon as fully accepted, and as part of a special issue when all submissions have completed their processes


Guest Editors:

Nigel Bosch

University of Illinois Urbana-Champaign, pnb@illinois.edu

Ibrahim Dahlstrom-Hakki

TERC, idahlstromhakki@terc.edu

Ryan S. Baker

University of Pennsylvania, rybaker@upenn.edu

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