Browsing by Author "Perez-Sanagustin, Mar"

Now showing 1 - 12 of 12
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    A Competency Framework for Teaching and Learning Innovation Centers for the 21st Century: Anticipating the Post-COVID-19 Age
    (2022) Perez-Sanagustin, Mar; Kotorov, Iouri; Teixeira, Antonio; Mansilla, Fernanda; Broisin, Julien; Alario-Hoyos, Carlos; Jerez, Oscar; Teixeira Pinto, Maria do Carmo; Garcia, Boni; Delgado Kloos, Carlos; Morales, Miguel; Solarte, Mario; Oliva-Cordova, Luis Magdiel; Gonzalez Lopez, Astrid Helena
    During the COVID-19 pandemic, most Higher Education Institutions (HEIs) across the globe moved towards "emergency online education", experiencing a metamorphosis that advanced their capacities and competencies as never before. Teaching and Learning Centers (TLCs), the internal units that promote sustainable transformations, can play a key role in making this metamorphosis last. Existing models for TLCs have defined the competencies that they could help develop, focusing on teachers', students', and managers' development, but have mislead aspects such as leadership, organizational processes, and infrastructures. This paper evaluates the PROF-XXI framework, which offers a holistic perspective on the competencies that TLCs should develop for supporting deep and sustainable transformations of HEIs. The framework was evaluated with 83 participants from four Latin American institutions and used for analyzing the transformation of their teaching and learning practices during the pandemic lockdown. The result of the analysis shows that the PROF-XXI framework was useful for identifying the teaching and learning competencies addressed by the institutions, their deficiencies, and their strategic changes. Specifically, this study shows that most institutions counted with training plans for teachers before this period, mainly in the competencies of digital technologies and pedagogical quality, but that other initiatives were created to reinforce them, including students' support actions.
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    Analytics of Learning Strategies: Role of Course Design and Delivery Modality
    (2020) Matcha, Wannisa; Gasevic, Dragan; Uzir, Nora'ayu Ahmad; Jovanovic, Jelena; Pardo, Abelardo; Lim, Lisa; Maldonado-Mahauad, Jorge; Gentili, Sheridan; Perez-Sanagustin, Mar; Tsai, Yi-Shan
    Generalizability of the value of methods based on learning analytics remains one of the big challenges in the field of learning analytics. One approach to testing generalizability of a method is to apply it consistently in different learning contexts. This study extends a previously published work by examining the generalizability of a learning analytics method proposed for detecting learning tactics and strategies from trace data. The method was applied to the datasets collected in three different course designs and delivery modalities, including flipped classroom, blended learning, and massive open online course. The proposed method combines process mining and sequence analysis. The detected learning strategies are explored in terms of their association with academic performance. The results indicate the applicability of the proposed method across different learning contexts. Moreover, the findings contribute to the understanding of the learning tactics and strategies identified in the trace data: learning tactics proved to be responsive to the course design, whereas learning strategies were found to be more sensitive to the delivery modalities than to the course design. These findings, well aligned with self-regulated learning theory, highlight the association of learning contexts with the choice of learning tactics and strategies.
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    Building Institutional Capacity for Learning Analytics: Top-Down & Bottom-Up Initiatives
    (2022) Perez-Sanagustin, Mar; Hilliger Carrasco, Isabel; Maldonado-Mahauad, Jorge; Perez-Alvarez, Ronald
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    Design of a Curriculum Analytics Tool to Support Continuous Improvement Processes in Higher Education
    (2020) Hilliger Carrasco, Isabel; Aguirre, Camila; Miranda, Constanza; Celis, Sergio; Perez-Sanagustin, Mar
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    Design of a Tool to Support Self-Regulated Learning Strategies in MOOCs
    (2018) Perez-Alvarez, Ronald; Maldonado Mahauad, Jorge Javier; Perez-Sanagustin, Mar
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    Fostering the use of online learning resources: results of using a mobile collaboration tool based on gamification in a blended course
    (2023) Ramirez-Donoso, Luis; Perez-Sanagustin, Mar; Neyem, Andres; Alario-Hoyos, Carlos; Hilliger Carrasco, Isabel; Rojos, Felipe
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    Insights in Learners' Behaviour and Early Dropout Detection based on Coursera MOOCs
    (Institute of Electrical and Electronics Engineers Inc., 2021) Frank, Sarah; Gutl, Christian; Perez-Sanagustin, Mar; Hilliger Carrasco, Isabel
    Increased utilization of distance learning makes the creation of effective learning tools ever more important. However, while Massive Open Online Courses (MOOCs) are a popular online learning tool, they often suffer from high user attrition. This paper investigated this effect for 6 different MOOCs with more than 35 thousand users, using AdaBoosted decision trees to create a model which was then used for the prediction of dropouts, as well as the calculation of feature importance scores. The resulting model generally scored high accuracy scores which were plotted for each course, and feature importance scores were especially high for the features encompassing the number of user requests, the user's total active time and the average time between clicks. Furthermore, the paper explored the results of the inclusion of forum data in this setting. While the forum features did not lead to a major increase in model accuracy, there was a statistical difference between the number of forum interactions for those who completed the MOOCs and those who did not, which opens up possibilities for future research into utilization of forum interaction data, and forums in MOOCs in general.
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    Large scale analytics of global and regional MOOC providers: Differences in learners' demographics, preferences, and perceptions
    (2022) Ruiperez-Valiente, Jose A.; Staubitz, Thomas; Jenner, Matt; Halawa, Sherif; Zhang, Jiayin; Despujol, Ignacio; Maldonado-Mahauad, Jorge; Montoro, German; Peffer, Melanie; Rohloff, Tobias; Lane, Jenny; Turro, Carlos; Li, Xitong; Perez-Sanagustin, Mar; Reich, Justin
    Massive Open Online Courses (MOOCs) remarkably attracted global media attention, but the spotlight has been concentrated on a handful of English-language providers. While Coursera, edX, Udacity, and FutureLearn received most of the attention and scrutiny, an entirely new ecosystem of local MOOC providers was growing in parallel. This ecosystem is harder to study than the major players: they are spread around the world, have less staff devoted to maintaining research data, and operate in multiple languages with university and corporate regional partners. To better understand how online learning opportunities are expanding through this regional MOOC ecosystem, we created a research partnership among 15 different MOOC providers from nine countries. We gathered data from over eight million learners in six thousand MOOCs, and we conducted a large-scale survey with more than 10 thousand participants. From our analysis, we argue that these regional providers may be better positioned to meet the goals of expanding access to higher education in their regions than the better-known global providers. To make this claim we highlight three trends: first, regional providers attract a larger local population with more inclusive demographic profiles; second, students predominantly choose their courses based on topical interest, and regional providers do a better job at catering to those needs; and third, many students feel more at ease learning from institutions they already know and have references from. Our work raises the importance of local education in the global MOOC ecosystem, while calling for additional research and conversations across the diversity of MOOC providers.
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    Lessons learned from designing a curriculum analytics tool for improving student learning and program quality
    (2022) Hilliger Carrasco, Isabel; Aguirre, Camila; Miranda, Constanza; Celis, Sergio; Perez-Sanagustin, Mar
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    Self-tracking Time-On-Task: Web-Based Weekly Timesheets for Higher Education Students
    (2021) Hilliger Carrasco, Isabel; Miranda, Constanza; Schuit, Gregory; Perez-Sanagustin, Mar
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    Supporting Decision-Making for Promoting Teaching and Learning Innovation: A Multiple Case Study
    (2024) Kotorov, Iouri; Krasylnykova, Yuliya; Perez-Sanagustin, Mar; Mansilla, Fernanda; Broisin, Julien
    The quality of the data and the amount of correct information available is key to informed decision -making. Higher education institutions (HEIs) often employ various decision support systems (DSSs) to make better choices. However, there is a lack of systems to assist with decision -making to promote innovation in teaching and learning. In this study, we evaluate an analytic tool called PROF-XXI that supports strategic decision -making of teaching and learning centres (TLCs) by identifying their competencies in teaching and learning innovation. Through a multiple case study conducted with three Latin American universities and supported by quantitative and qualitative data, we observed how this tool is used and how it facilitates strategic decision -making. Our findings indicate that the tool is accessible, user-friendly, and effective in 1) initiating identification and systematic reflection of institutional competency levels in teaching and learning innovation, 2) enhancing understanding of strengths and weaknesses as well as identifying opportunities for innovation, 3) supporting TLCs with short- and long-term decision -making, and 4) continuously evaluating their strategies, programs, and initiatives. This research can benefit policymakers in higher education who are involved in measuring institutional competencies to improve teaching quality or in making strategic decisions related to teaching and learning innovation.
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    Understanding Learners' Motivation and Learning Strategies in MOOCs
    (2017) Alario-Hoyos, Carlos; Estevez-Ayres, Iria; Perez-Sanagustin, Mar; Delgado Kloos, Carlos; Fernandez-Panadero, Carmen
    MOOCs (Massive Open Online Courses) have changed the way in which OER (Open Educational Resources) are bundled by teachers and consumed by learners. MOOCs represent an evolution towards the production and offering of structured quality OER. Many institutions that were initially reluctant to providing OER have, however, joined the MOOC wave. Nevertheless, MOOCs detractors strongly criticize their high dropout rates. The dropout rate is a commonly accepted metric of success for traditional education, but it may not be as suitable when dealing with OER, in general, and with MOOCs, in particular, since learners' motivations to take a course are very diverse, and certain selfregulated learning strategies are required to tackle the lack of personalized tutoring and keep pace in the course. This paper presents an empirical study on the motivation and learning strategies of MOOC learners. Six thousand three hundred and thirty-five learners from 160 countries answered a selfreport 7-point Likert-type questionnaire based on the Motivated Strategies for Learning Questionnaire (MSLQ) as part of a MOOC titled Introduction to Programming with Java. Results indicate that learners were highly motivated and confident to do well in the course. Learning strategies, however, can be improved, especially regarding time management.