Author: scupelli

Paper presentation @ EuroPLoP 2015 writer’s workshop

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July 11, 2015 3:54 PM

Peter Scupelli and Paul Inventado presented “Data-Driven Design Pattern Production: A Case Study on the ASSISTments Online Learning System” in a writing workshop 20th European Conference on Pattern Languages of Programs (EuroPLoP) 2015 in Bavaria, Germany.

Abstract:

Online learning systems popularity increased rapidly in recent decades in multiple domains such as cognitive tutors, online courses, and massive open online courses (MOOCS). The design quality of online learning systems is difficult to maintain. Multiple stakeholders are involved (e.g., software developers, interaction designers, learning scientists, teachers), the system is complex, there are rapid changes in software, platforms (e.g., mobile, tablet, desktop) and learning subject content, and so forth. Many existing online learning systems collect a significant amount of data that describe learning outcomes and student behaviors, which are indirect measures of system quality. Data analysis on online learning systems data can uncover linkages between particular design choices made and student learning outcomes. In this paper, we describe the Data-Driven Design Patterns Production (3D2P) methodology to prospect, mine, write and evaluate design patterns for online learning systems. Pattern prospecting helps designers decide what type of possible meaningful outcomes and features to scan for in the data and helps to focus on specific data subsets to limit the search space for pattern mining. Design patterns identified with 3D2P methodology can guide the addition of new content and the modification of system designs to maintain the online learning system’s quality. We present a case study of the ASSISTments math online learning system to illustrate the 3D2P methodology and discuss its benefits and limitations.

Last updated: 3:54 pm

New publication in eLearning Papers #42

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June 15, 2015 7:56 PM

Paul Salvador Inventado and Peter Scupelli’s paper entitled “Towards an open, collaborative repository for online learning system design patterns” has been published in eLearning Papers #42.

Abstract:

Design patterns are high-quality solutions to known problems in a specific context that guide design decisions. Typically, design patterns are mined and evaluated through four methods: expert knowledge, artifact analysis, social observations, and workshops. For example, experts discuss: knowledge, interpretations of artifacts, social patterns, and clarity of patterns. In this paper, we introduce a fifth method, a data-driven design pattern production (3D2P) method to produce design patterns and conduct randomized controlled trials as a means to evaluate applied design patterns. We illustrate the 3D2P method in the context of online learning systems (OLSs) that are difficult to create, update and maintain. To overcome such challenges, we propose an open repository for OLS design patterns, evaluation data, and implementation examples. On the repository, researchers can collaborate in the six stages of the pattern lifecycle (i.e., prospecting, mining, writing, evaluation, application, applied evaluation). The repository provides five benefits: researchers from different backgrounds can (a) collaborate on design pattern production; (b) perform distributed tasks in parallel; (c) share results for mutual benefit; (d) test patterns across varied systems and domains to explore pattern generalizability and robustness; and (e) promote design patterns to elevate OLS quality.

Last updated: 7:56 pm

Design Studio Learning Environment Research

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November 14, 2014 9:00 AM

Studio-based design education is changing to include multidisciplinary design teams, geographically distributed teams, information technology, and new work styles. In this research, we describe the graduate design studio redesign in the School of Design at Carnegie Mellon University. The old graduate studio went from a single room design studio to four interconnected spaces: an area with individual workspaces, collaborative spaces, a kitchen and social cafe area, and a classroom with distance learning technology.

  • Study 1 indicates student satisfaction significantly improved. However, open-ended survey comments suggest that functional needs were met, but some pleasure-related and emotional needs linked to habitation were problematic.
  • Study 2 explores ownership, personalization, aesthetics, function, acoustics, upkeep, and agency in the four connected studio spaces (i.e., individual workspaces, collaborative spaces kitchen and social cafe area, the distance learning classroom). Research methods included an online survey and desk interviews.
  • Study 3 determines student occupancy levels in the design studio spaces via a time-lapse study. One picture is taken every minute to determine where students work in the four interconnected spaces.

Key findings include: (a) users evaluated studio spaces holistically based on functionality, emotional response, and pleasure; (b) owned spaces differed significantly from shared spaces; (c) individual work and collaboration work occurred throughout the studio (e.g., collaboration in quiet individual workspaces, and individual work in loud collaboration spaces). The research approach above informs the study of IDeATE studio-learning spaces.

Principal Contact

Peter Scupelli, Ph.D.
Assistant Professor in IxD
School of Design
scupelli@cmu.edu

Research Team

Bruce Hanington
Associate Professor & Head of Graduate Studies
School of Design

Andrea Fineman
Graduate Research Assistant
School of Design

Xiaowei Jiang
Graduate Research Assistant
School of Design

Frances Yin Wang
Graduate Research Assistant
School of Design

Collaborative spaces and individual workspaces in design studios: a study on ownership, personalization, agency, emotion, and pleasure

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October 24, 2014 9:00 AM

Studio-based design education is changing to include multidisciplinary design teams, geographically distributed teams, information technology, and new work styles. In this talk, I describe the graduate design studio redesign in the School of Design at Carnegie Mellon University. The old studio went from a single room design studio to four interconnected spaces: an area with individual workspaces, collaborative spaces, a kitchen and social cafe area, and a classroom with distance learning technology. Study one indicates student satisfaction significantly improved but some open-ended survey comments suggest that functional needs were met, but some pleasure-related and emotional needs linked to habitation were problematic. Study two used an online survey and a time-lapse study to explore ownership, personalization, aesthetics, function, acoustics, upkeep, and agency in the four connected studio spaces: individual workspaces, collaborative spaces kitchen and social cafe area, and the distance learning classroom. Don Norman’s Emotional Design and Patrick Jordan’s Designing Pleasurable Products books are used as frameworks to explore user needs in design studios.

Last updated: 9:00 am

Data-driven Design Pattern Development (3DPD) Workshop @ PLoP 2014

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July 8, 2014 5:05 PM

Peter Scupelli and Paul Inventado facilitated the Data-driven Design Pattern Development (3DPD) Workshop which was  held in conjunction with the 21st Conference on Pattern Languages of Programs (PLOP 2014).

Abstract:

Workshop description: Increasingly, outside of the design pattern community, big data is being used to validate design decisions. For example, A/B testing and simple randomized experiments with two variants are used to validate design decisions in online settings such as web-design, social media, and so forth. Likewise, in online math tutor systems, data-driven approaches are used to identify math problems that confuse and frustrate students or are linked to positive learning outcomes. Big data and measurable outcomes can help to identify problems and confirm high-quality solutions. Design patterns provide a very effective way to describe known design problems and design solutions. In this workshop, we propose to explore the role of data-driven processes in the development of design patterns, such that data-driven exploration may help authors uncover problems and high-quality solutions. In the workshop, we explore domains where data-driven approaches may be appropriate to develop design patterns, methodologies for collecting data, techniques for pre-processing data, approaches for analyzing data and utilizing its results to facilitate the design pattern development process. Participants will have the opportunity to experience the data-driven design pattern development approach through group discussions and simulations.

Last updated: 5:05 pm