Exploring Collaborative Writing of User Stories With Multimodal Learning Analytics: A Case Study on a Software Engineering Course

Noël, René; Riquelme, Fabián; Lean, Roberto Mac; Merino, Erick; Cechinel, Cristian; Barcelos, Thiago; Villarroel, Rodolfo; Castro-Muñoz, Roberto

doi:10.1109/access.2018.2876801

Cited by 30 publications

(33 citation statements)

References 32 publications

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“…Worsley and Blikstein ( 2018 ) argued for MMLA by suggesting that the existing strategies for analyzing MMD could provide more meaningful insights into complex learning processes than traditional approaches can. In the same vein, Spikol, Ruffaldi, Dabisias, and Cukurova ( 2018 ) and Blikstein and Worsley ( 2016 ) highlighted the importance and benefits of MMD in open-ended learning tasks, while Noel et al . ( 2018 ) and Liu et al .…”

Section: Practitioner Notesmentioning

confidence: 99%

“…This could further showcase the exploratory phase of MMLA research since most of the contributions present an analytical/conceptual framework, and then, used the data collected from a study as an example "case study." For example, some studies proposed a method to obtain relevant predictions from the MMD and used a data collection based on a certain context as a case study (Sharma, Papamitsiou, et al ., 2019 ), or they proposed a method to use MMD to understand user stories in a software engineering course and used the data collected from a specific case study to test the proposed method (Noel et al ., 2018 ). The majority of the case studies (27) were conducted in formal learning settings, with a quarter of them (11) being in informal learning environments (four of them did not explicitly mention the learning settings).…”

Section: Research Findings Domain Population and Research Topicmentioning

confidence: 99%

“…In collaborative conditions, MMD have been used to identify key moments of collaboration (Noel et al ., 2018 ;Noroozi et al ., 2019 ;Pijeira-Díaz, Drachsler, Järvelä, & Kirschner, 2019 ). MMD have also been used to distinguish between help-seeking and help-giving behavior (Cukurova, Kent, & Luckin, 2019 ), tentative and casual problem-solving behavior (Andrade et al ., 2017 ), nonverbal behaviors (Cukurova, Luckin, Millán, & Mavrikis, 2018 ), solving versus guessing (Sharma, Papamitsiou, et al ., 2019 ) and the reasoning behavior of students (Worsley & Blikstein, 2015, 2018.…”

Section: For Learning Behavior and Performancementioning

confidence: 99%

“…With regard to the second mapping ( Figure 6 , right), we observe four clear trends. First, while analyzing a collaborative and formal setting, certain combinations of audio, video, physiological data and system logs were used (Martinez-Maldonado, Power, et al ., 2017 ;Noel et al ., 2018 ). Second, for informal situations in collaborative learning, audio, system logs, gesture and motion data were preferred (Spikol et al ., 2016(Spikol et al ., , 2018.…”

Section: Opportunitiesmentioning

confidence: 99%

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Multimodal data capabilities for learning: What can multimodal data tell us about learning?

Sharma¹,

Giannakos²

2020

Brit J Educational Tech

113

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Most research on learning technology uses clickstreams and questionnaires as their primary source of quantitative data. This study presents the outcomes of a systematic literature review of empirical evidence on the capabilities of multimodal data (MMD) for human learning. This paper provides an overview of what and how MMD have been used to inform learning and in what contexts. A search resulted in 42 papers that were included in the analysis. The results of the review depict the capabilities of MMD for learning and the ongoing advances and implications that emerge from the employment of MMD to capture and improve learning. In particular, we identified the six main objectives (ie, behavioral trajectories, learning outcome, learning‐task performance, teacher support, engagement and student feedback) that the MMLA research has been focusing on. We also summarize the implications derived from the reviewed articles and frame them within six thematic areas. Finally, this review stresses that future research should consider developing a framework that would enable MMD capacities to be aligned with the research and learning design (LD). These MMD capacities could also be utilized on furthering theory and practice. Our findings set a baseline to support the adoption and democratization of MMD within future learning technology research and development. What is already known about this topic Capturing and measuring learners’ engagement and behavior using MMD has been explored in recent years and exhibits great potential. There are documented challenges and opportunities associated with capturing, processing, analyzing and interpreting MMD to support human learning. MMD can provide insights into predicting learning engagement and performance as well as into supporting the process. What this paper adds Provides a systematic literature review (SLR) of empirical evidence on MMD for human learning. Summarizes the insights MMD can give us about the learning outcomes and process. Identifies challenges and opportunities of MMD to support human learning. Implications for practice and/or policy Learning analytics researchers will be able to use the SLR as a guide for future research. Learning analytics practitioners will be able to use the SLR as a summary of the current state of the field.

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Section: Practitioner Notesmentioning

confidence: 99%

Section: Research Findings Domain Population and Research Topicmentioning

confidence: 99%

Section: For Learning Behavior and Performancementioning

confidence: 99%

Section: Opportunitiesmentioning

confidence: 99%

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Multimodal data capabilities for learning: What can multimodal data tell us about learning?

Sharma¹,

Giannakos²

2020

Brit J Educational Tech

113

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“…Furthermore, MMLA seeks to quantify what was not quantifiable before and understand people's behavior from the measurement of specific skills. MMLA techniques have recently been used to evaluate the collaboration of individuals in learning environments [3], [7], in programming tasks [8] and in the training of agile practices [1], [9]. According to Worsley [3], one of the MMLA techniques that has been less explored and developed is micro-location.…”

Section: Introductionmentioning

confidence: 99%

Where are You? Exploring Micro-Location in Indoor Learning Environments

et al. 2020

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Classroom teaching methodologies are gradually changing from masterclasses to active learning practices, and peer collaboration emerges as an essential skill to be developed. However, there are several challenges in evaluating collaborative activities more objectively, as well as to generate valuable information to teachers and appropriate feedback to students about their learning processes. In this context, multimodal learning analytics facilitate the evaluation of complex skills using data from multiple data sources. In this work, we propose the use of beacons to collect geolocation data from students who carry out collaborative tasks that involve movement and interactions through space. Furthermore, we suggest new ways to analyze, visualize, and interpret the data obtained. As a first practical approach, we carried out an exploratory, collaborative activity with sixteen undergraduate students working in a library, with bookshelves and work tables monitored by beacons. From the analysis of student movement dynamics, three types of well-differentiated student roles were identified: the collectors, those who go out to collect data from the bookshelves, ambassadors, those who communicate with other groups, and the secretaries, those who stay at their work desk to shape the requested essay. We believe these findings are valuable feedback for the enhancement of the learning activity and the first step towards MMLA-driven Teaching Process Improvement method.

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USQA: A User Story Quality Analyzer prototype for supporting software engineering students

Jiménez¹,

Alanis

Beltrán

et al. 2023

Comp Applic In Engineering

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The Standish Group Reports 83.9% of IT Projects fail, and one of the top factors in failed projects is the incomplete requirements or user stories. Therefore, it is essential to teach undergraduate students from computer science degree programs how to create complete user stories. Computer science programs include some subjects or topics involving requirements or user stories collection and writing, such as Requirements Engineering, Software Engineering, Project Management, or Quality Software Assurance. For that reason, we designed a web application called User Story Quality Analyzer (USQA) that uses Natural Language Processing modules to detect errors regarding aspects of usefulness, completeness, and polysemes in the user stories creation. The tool was proved from three perspectives: (1) a reliability test, where 35 user stories developed by experts were tested in the app to prove the prototype's reliability; (2) usability and utility analysis; 48 students interacted with the tool and responded a Satisfaction Usability Scale and an open‐ended question, the students reported a high usability score; (3) finally, error classification, we gathered 159 user stories processed by the system, and we classified the students' common errors considering incompleteness and polysemes. After the evaluations, we concluded that USQA could evaluate the user stories as an expert, which could help the professors/teachers/instructors in their courses by providing feedback to the students when they are writing user stories.

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Exploring Collaborative Writing of User Stories With Multimodal Learning Analytics: A Case Study on a Software Engineering Course

Cited by 30 publications

References 32 publications

Multimodal data capabilities for learning: What can multimodal data tell us about learning?

Multimodal data capabilities for learning: What can multimodal data tell us about learning?

Where are You? Exploring Micro-Location in Indoor Learning Environments

USQA: A User Story Quality Analyzer prototype for supporting software engineering students

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