Evidence-based Best Practices for First-year Blended Learning Implementation

Marasco, Emily; Moshirpour, Mohammad; Moussavi, Mahmood; P.Eng., Laleh Behjat; Amannejad, Yasaman

doi:10.18260/1-2--30457

Cited by 9 publications

(14 citation statements)

References 3 publications

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“…Some findings: Viewing long static videos can be boring and course overwhelming [11]. Students generally perceive the online lectures as helpful [34]. Intrinsic motivation did not impact performance in the flipped classroom [37].…”

Section: G Findingsmentioning

confidence: 96%

“…One experiment uses the results of ten different universities [19]. There are reports of units from twenty-five institutions: Arizona State University College of Technology [11], Boston College [39], Brandeis University [29] [31], Bucknell University [40], Central Michigan University [28], Clark State Community College [38], CSU Monterey Bay [14], Indiana University [22], [41], Karadeniz Technical University (Turkey) [27], [23], Nazarbayev University, Kazakhstan [35], [42], Prince of Songkla University (Thailand) [26], Qatar University [43], Science and Technology of South of Minas Gerais [44], Singapore Management University [30], St. Joseph's College of Bangalore [25], Texas A&M University at Qatar [45], Trinity University [18], [46], University of British Columbia [21], University of Calgary [33], [34], University of Hartford [47], University of Helsinki [12], University of North Carolina [12], [36], [37], University of Pittsburgh [11], University of San Diego [20] and University of Toronto [15]- [17].…”

Section: Higher Education Institutionsmentioning

confidence: 99%

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Flipped Classrooms for Introductory Computer Programming Courses

Sobral¹

2021

IJIET

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Teaching and learning how to program are not easy tasks. Disapproval and dropout rates are a concern for everyone concerned with the topic. Therefore, it is necessary to look for strategies that improve the motivation of students who start a programming course, also improving success rates and decreasing dropout rates. The inverted class model, or flipped classroom, has been used in several experiments, showing very good results. The objectives of this teaching-learning technique is to change the traditional order: students have contact with new subjects before the classroom, using videos, texts or other material, as well as small online tests to check their knowledge. In this way, the face-to-face classes are reserved for discussion, doubts and application of previously acquired knowledge. The purpose of this paper is to analyze the scientific production on Flipped classrooms in introductory programming courses indexed in Elsevier’s Scopus. The sample is composed by 45 articles in total. The results obtained by bibliometric analysis showed when and where those documents are published, who are the authors and what is the focus of said articles. We also analyzed the most cited documents. We made a summary of the articles, namely in what refers to the sample size of the experiences, which programming language is used, in which universities the articles are made, which technology is used, as well as which methods are used in order to create inverted classes and which are the objectives and results of these experiences reported on the articles.. We managed to get a global view of the theme, getting a strong analysis for those who want to use flipped classrooms for teaching programming.

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Section: G Findingsmentioning

confidence: 96%

Section: Higher Education Institutionsmentioning

confidence: 99%

Flipped Classrooms for Introductory Computer Programming Courses

Sobral¹

2021

IJIET

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“…Hui et al, 2018;Kravchenko and Cass, 2017) or focus groups (e.g, Ayres et al, 2018;Kaw et al, 2019), or in-class observation based on the action research methodology (Isomöttönen and Tirronen, 2017). Some studies also investigated the lived experiences of students through self-reported opinion or motivation questionnaires (Marasco et al, 2018;Lau et al, 2018;Lei et al, 2017;Chu, Wang and Wang, 2019 However, although mixed methods are applicable to the observation of the FC experience, the core of the data was quantitative and used to apply LA algorithms. Figure 6 presents the type of algorithm applied in the LA phase.…”

Section: Figure 3: Distribution Of Studies By Year Of Publicationmentioning

confidence: 99%

“…Twelve articles out of 49 presented similar issues, which we can decompose into the following specific problematics: Quality of engagement: several studies (e.g. Marasco et al, 2018) underlined that the measures from LA were mostly click-based, and did not evaluate the quality of students' engagement with the learning material.…”

Section: 3mentioning

confidence: 99%

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Learning Analytics in Flipped Classrooms: A Scoping Review

Algayres

Triantafyllou

2020

ECEL

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The Flipped Classroom (FC) is an instruction method, where the traditional lecture and homework sessions are inverted. Online material is given to students in order to gain necessary knowledge before class, while class time is devoted to application of this knowledge and reflection. The hypothesis is that there could be deep and creative discussions when teacher and students physically meet, which has known a significant surge of popularity in the past decade. A marked recent trend in the FC is the increased use of Learning Analytics (LA) to support the development of the FC and students’ reflexive learning. The aim of this paper is to investigate the literature on applications of LA in FCs, and to determine the best practices and needs for technological development supporting LA in the FC by means of a scoping review. This literature review revealed that there is potential in using LA in the FC, especially as a means to predict students’ learning outcome and to support adaptive learning and improvement on the curriculum. However, further long‑term studies and development is necessary to encourage self‑directed learning in students and to develop the whole of the FC for a more diverse population of students. We anticipate an increased and expanded use of LA to come, with focus on predictive and prescriptive analytics providing more adaptive learning experience. We also anticipate that LA will expand beyond data mining to correlate student performance and online engagement with the aim to include a wider range of possibilities of interventions and adaptation of the learning experience.

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Analyzing the Group Formation Process in Intelligent Tutoring Systems

Rubio-Fernández

Muñoz‐Merino

Kloos

2019

Intelligent Tutoring Systems

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XI 3.2.3. Análisis de los grupos focales con los estudiantes․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․76 3.2.4. Conclusiones del análisis de las entrevistas y de los grupos focales․․․․․․․․․76 3.2.4.1. Entorno de aprendizaje adaptable y personalizado․․․․․․․․․․․․․․․․․․․․․․․․77 3.2.4.2. Mejora de la comunicación entre profesores y estudiantes․․․․․․․․․․․․․․․80 Capítulo 4. Propuesta de marco para la combinación de analítica de aprendizaje y la clase invertida․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․84 4.1. Indicadores utilizados en el marco de trabajo․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․86 4.1.1 Indicadores asociados con los vídeos․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․93 4.1.1.1. Indicadores asociados a un único estudiante․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․93 4.1.1.2. Indicadores asociados a varios estudiantes․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․94 4.1.2. Indicadores asociados con los ejercicios․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․101 4.1.2.1. Indicadores asociados a un único estudiante․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․101 4.1.2.2. Indicadores asociados a varios estudiantes․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․102 4.1.3. Indicadores asociados con las lecturas․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․107 4.1.4. Indicadores asociados con los foros de discusión․․․․․․․․․․․․․․․․․․․․․․․․․․․․․108 4.2. Uso de los indicadores en los diferentes tipos de clase invertida․․․․․․․․․․․․․․․․․109 4.2.1. Clase invertida orientada a ejercicios․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․110 4.2.1.1. Antes de la clase presencial․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․113 4.2.1.2. Durante la clase presencial․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․114 4.2.1.3. Después de la clase presencial․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․114 4.2.2. Clase invertida orientada a actividades en grupo․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․115 4.2.2.1. Antes de la clase presencial․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․117 4.2.2.2. Durante la clase presencial․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․118 4.2.2.3. Después de la clase presencial․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․118 4.2.3. Clase invertida orientada a prácticas experimentales․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․119 4.2.3.1. Antes de la clase presencial․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․121 4.2.3.2. Durante la clase presencial․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․122 4.2.3.3. Después de la clase presencial․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․123 4.2.4. Clase invertida orientada a la creación de recursos educativos por parte del _________estudiante․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․124 4.2.4.1. Antes de la clase presencial․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․.126 4.2.4.2. Durante la clase presencial․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․.127 4.2.4.3. Después de la clase presencial․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․․127XII 4.2.5. Comparativa de los indicadores seleccionados según el modelo de clase _________invertida utilizado...

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Evidence-based Best Practices for First-year Blended Learning Implementation

Cited by 9 publications

References 3 publications

Flipped Classrooms for Introductory Computer Programming Courses

Flipped Classrooms for Introductory Computer Programming Courses

Learning Analytics in Flipped Classrooms: A Scoping Review

Analyzing the Group Formation Process in Intelligent Tutoring Systems

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