2018 Proceeding- Conference Chair:
- Rose Luckin,
- Program Chairs:
- Scott Klemmer,
- Kenneth Koedinger
It is our great pleasure to present the Proceedings of the Fifth Annual ACM Conference on Learning at Scale, L@S 2018, held on June 26-28 at UCL Institute of Education, London, UK.
Learning at Scale investigates large-scale, technology-mediated learning environments. The conference was created by the Association for Computing Machinery (ACM), inspired by the emergence of Massive Open Online Courses (MOOCs) and the accompanying shift in thinking about education. The conference was originally inspired by the emergence of MOOCs, but it is by no means limited to MOOCs. Large-scale technology mediated learning environments are diverse and the conference series is a venue for discussion of the highest quality research on how learning and teaching can be transformed by that diversity of environments. Intelligent tutoring systems, open learning courseware, learning games, citizen science communities, collaborative programming communities, community tutorial systems, and the countless informal communities of learners are all examples of learning at scale. These systems either depend upon large numbers of learners, or they are enriched through use of data generated by the previous use of many learners. They share a common purpose--to increase human potential--and a common infrastructure of data and computation to enable learning at scale.
Investigations of learning at scale naturally bring together two different research communities. Learning scientists are drawn to these innovative environments to study established and emerging forms of knowledge production, transfer, modeling, and co-creation. Computer scientists are drawn to the field as powerful site for the development and application of advanced computational techniques. Learning at Scale brings these communities together and supports interdisciplinary investigation and innovation that draws upon or contributes to both scientific insight and technological advances.
The goal of the Learning at Scale community is the understanding and enhancement of human learning. In emerging education technology genres, researchers often use a variety of proxy measures for learning, including measures of participation, persistence, completion, satisfaction, and activity. In the early stages of investigating a technological genre, it is entirely appropriate to begin lines of research by investigating these proxy outcomes. As lines of research mature, however, it is important for the community of researchers to hold each other to increasingly high standards and expectations for directly investigating thoughtfully constructed measures of learning. In the early days of research on MOOCs, many researchers documented correlations between measures of activity (videos watched, forums posted, clicks) and outcome proxies including participation, persistence, and completion. As learning at scale research matures, studies that document these kinds of correlations should give way to studies of student learning that produce evidence of instructional techniques, technological infrastructures, learning habits, and experimental interventions that improve learning. As a community, we believe that that the very best of our early papers define a foundation to build upon but are not an established standard to which to aspire.
Proceeding Downloads
Replicating MOOC predictive models at scale
We present a case study in predictive model replication for student dropout in Massive Open Online Courses (MOOCs) using a large and diverse dataset (133 sessions of 28 unique courses offered by two institutions). This experiment was run on the MOOC ...
Students, systems, and interactions: synthesizing the first four years of learning@scale and charting the future
We survey all four years of papers published so far at the Learning at Scale conference in order to reflect on the major research areas that have been investigated and to chart possible directions for future study. We classified all 69 full papers so ...
The potential for scientific outreach and learning in mechanical turk experiments
The global reach of online experiments and their wide adoption in fields ranging from political science to computer science poses an underexplored opportunity for learning at scale: the possibility of participants learning about the research to which ...
Toward large-scale learning design: categorizing course designs in service of supporting learning outcomes
This paper applies theory and methodology from the learning design literature to large-scale learning environments through quantitative modeling of the structure and design of Massive Open Online Courses. For two institutions of higher education, we ...
Addressing two problems in deep knowledge tracing via prediction-consistent regularization
Knowledge tracing is one of the key research areas for empowering personalized education. It is a task to model students' mastery level of a knowledge component (KC) based on their historical learning trajectories. In recent years, a recurrent neural ...
The effects of adaptive learning in a massive open online course on learners' skill development
- Yigal Rosen,
- Ilia Rushkin,
- Rob Rubin,
- Liberty Munson,
- Andrew Ang,
- Gregory Weber,
- Glenn Lopez,
- Dustin Tingley
We report an experimental implementation of adaptive learning functionality in a self-paced Microsoft MOOC (massive open online course) on edX. In a personalized adaptive system, the learner's progress toward clearly defined goals is continually ...
QG-net: a data-driven question generation model for educational content
The ever growing amount of educational content renders it increasingly difficult to manually generate sufficient practice or quiz questions to accompany it. This paper introduces QG-Net, a recurrent neural network-based model specifically designed for ...
Towards domain general detection of transactive knowledge building behavior
Support of discussion based learning at scale benefits from automated analysis of discussion for enabling effective assignment of students to project teams, for triggering dynamic support of group learning processes, and for assessment of those learning ...
Docent: transforming personal intuitions to scientific hypotheses through content learning and process training
People's lived experiences provide intuitions about health. Can they transform these personal intuitions into testable hypotheses that could inform both science and their lives? This paper introduces an online learning architecture and provides system ...
Supporting answerers with feedback in social Q&A
Prior research has examined the use of Social Question and Answer (Q&A) websites for answer and help seeking. However, the potential for these websites to support domain learning has not yet been realized. Helping users write effective answers can be ...
Refocusing the lens on engagement in MOOCs
Massive open online courses (MOOCs) continue to see increasing enrollment and adoption by universities, although they are still not fully understood and could perhaps be significantly improved. For example, little is known about the relationships ...
The relationship between scientific explanations and the proficiencies of content, inquiry, and writing
Examining the interaction between content knowledge, inquiry proficiency, and writing proficiency is central to understanding the relative contribution of each proficiency on students' written communication about their science inquiry. Previous studies, ...
Towards making block-based programming activities adaptive
Block-based environments are today commonly used for introductory programming activities like those that are part of the Hour of Code campaign, which reaches millions of students. These activities typically consist of a static series of problems. Our ...
Towards adapting to learners at scale: integrating MOOC and intelligent tutoring frameworks
Instruction that adapts to individual learner characteristics is often more effective than instruction that treats all learners as the same. A practical approach to making MOOCs adapt to learners may be by integrating frameworks for intelligent tutoring ...
Combining adaptivity with progression ordering for intelligent tutoring systems
Learning at scale (LAS) systems like Massive Open Online Classes (MOOCs) have hugely expanded access to high quality educational materials however, such material are frequently time and resource expensive to create. In this work we propose a new ...
Toward a large-scale open learning system for data management
This paper describes ClassDB, a free and open source system to enable large-scale learning of data management. ClassDB is different from existing solutions in that the same system supports a wide range of data-management topics from introductory SQL to ...
From online learning to offline action: using MOOCs for job-embedded teacher professional development
Over two iterations of a Massive Open Online Course (MOOC) for school leaders, Launching Innovation in Schools, we developed and tested design elements to support the transfer of online learning into offline action. Effective professional learning is ...
Exploring the utility of response times and wrong answers for adaptive learning
Personalized educational systems adapt their behavior based on student performance. Most student modeling techniques, which are used for guiding the adaptation, utilize only the correctness of student's answers. However, other data about performance are ...
Measuring item similarity in introductory programming
A personalized learning system needs a large pool of items for learners to solve. When working with a large pool of items, it is useful to measure the similarity of items. We outline a general approach to measuring the similarity of items and discuss ...
Pilot study on optimal task scheduling in learning
Living in an information era where various online learning contents are rapidly available, students often learn with a combination of multiple learning tasks. In this work we explore the possibilities of using optimization theory to find the optimal ...
Longitudinal trends in sentiment polarity and readability of an online masters of computer science course
In four years, the Georgia Tech Online MS in CS (OMSCS) program has grown from 200 students to over 6000. Despite early evidence of success, there is a need to evaluate the program's effectiveness. In this paper, we focus on trends from Fall 2014 to ...
Glanceable code history: visualizing student code for better instructor feedback
Immediate, individualized feedback on their code helps students learning to program. However, even in short, focused exercises in active learning, teachers do not have much time to write feedback. In addition, only looking at a student's final code ...
Toward large-scale automated scoring of scientific visual models
Visual models of scientific concepts drawn by students afford expanded opportunities for showing their understanding beyond textual descriptions, but also introduce other elements characterized by artistic creativity and complexity. In this paper, we ...
An active viewing framework for video-based learning
Video-based learning is most effective when students are engaged with video content; however, the literature has yet to identify students' viewing behaviors and ground them in theory. This paper addresses this need by introducing a framework of active ...
A content engagement score for online learning platforms
- Vivek Singh,
- Balaji Padmanabhan,
- Triparna de Vreede,
- Gert-Jan de Vreede,
- Stephanie Andel,
- Paul E. Spector,
- Steve Benfield,
- Ahmad Aslami
Engagement on online learning platforms is essential for user retention, learning, and performance. However, there is a paucity of research addressing latent engagement measurement using user activities. In this work in progress paper, we present a ...
Adaptive natural-language targeting for student feedback
In tutoring software, targeting feedback to students' natural-language inputs is a promising avenue for making the software more effective. As a case study, we built such a system using Natural Language Processing (NLP) to provide adaptive feedback to ...
Rain classroom: a tool for blended learning with MOOCs
We present our implementation of a software system that facilitates teachers to create preview and review teaching materials before and after class, as well as enhance interactions between teachers and students for in-class activities. The software ...
Gamifying higher education: enhancing learning with mobile game app
We present a mobile game app (EUR Game) that has been designed to complement teaching and learning in higher education. The mobile game app can be used by teachers to gauge how well students are meeting the learning objectives. Teachers can use the ...
WPSS: dropout prediction for MOOCs using course progress normalization and subset selection
There are existing multi-MOOC level dropout prediction research in which many MOOCs' data are involved. This generated good results, but there are two potential problems. On one hand, it is inappropriate to use which week students are in to select ...
Contemporary online course design recommendations to support women's cognitive development
Originally, online higher education was imagined to be a utopia for women because gender would be less salient when learners were not physically co-present and thus gender power issues would be lessened. Unfortunately, gender power is present in online ...
Cited By
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Wang Y and Gupta H (2024). A Mass‐Conserving‐Perceptron for Machine‐Learning‐Based Modeling of Geoscientific Systems, Water Resources Research, 10.1029/2023WR036461, 60:4, Online publication date: 1-Apr-2024.
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Chaturapruek S, Johari R, Kizilcec R, Stevens M, Bernstein M, Cina S, Harrison M, Lifschitz A, Mitchell J, Paepcke A and Thompson M Understanding Undergraduate Course Consideration, SSRN Electronic Journal, 10.2139/ssrn.3432748
