Effectiveness of Innovate Educational Practices with Flipped Learning and Remote Sensing in Earth and Environmental Sciences—An Exploratory Case Study
"> Figure 1
<p>UAV (unmanned aerial vehicle) used for remote sensing.</p> "> Figure 2
<p>Aerial view of landforms and the coast: (<b>a</b>) UAVs allow students to know the complexity of the terrestrial landscape, the characteristics of elevations and depressions in the lithosphere. They also allow in-depth observation of the great forms of the relief: ancient massifs, sedimentary basins, or sedimentary plains and mountain ranges of recent formation. (<b>b</b>) UAVs used for remote sensing allow students live knowledge about the two main phenomena of marine erosion: sea currents and waves. Also, this tool makes it possible to know the effects of maritime erosion: cliffs, abrasion platforms, coastlines, sea caves, and peninsulas.</p> "> Figure 3
<p>Comparison between control groups and experimental groups.</p> ">
Abstract
:1. Introduction
1.1. Characteristics of the FL Approach
1.2. Use of Remote Sensing in Learning Spaces
- −
- Development of maps and realization of tours at a geographical level;
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- Calculation of distances, geometry, and temporal graphs in mathematics;
- −
- Development of multidisciplinary projects that include photographs, murals, and maps;
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- UAV manufacturing for students of specific subjects, such as trigonometry, robotics, programming, or electronics;
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- Recording of events for inclusion in digital and written media (school blog, university newspaper, educational television);
- −
- Development of fine motor skills and hand-eye coordination through specific UAV driving along an obstacle course;
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- Recording of physical education sessions for the optimization of student exercises, strategies, and movements.
1.3. Justification and Objectives
2. Materials and Methods
2.1. Research Design and Data Analysis
2.2. Participants
2.3. Instrument
2.4. Procedure
3. Results
4. Discussion and Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Group | n | Composition | Pretest | Treatment | Post-Test |
---|---|---|---|---|---|
1—Control | 30 | Natural | - | - | O1 |
2—Experimental | 29 | Natural | - | X | O2 |
Likert Scale n (%) | Parameters | ||||||||
---|---|---|---|---|---|---|---|---|---|
Variables | None | Few | Enough | Completely | M | SD | Skw | Kme | |
Control group | Motivation | 8(26.7) | 10(33.3) | 8(26.7) | 4(13.3) | 2.27 | 1.11 | 0.266 | −0.975 |
Teacher-student | 6(20) | 16(53.3) | 6(20) | 2(6.7) | 2.13 | 0.819 | 0.547 | 0.201 | |
Student-content | 11(36.7) | 12(40) | 4(13.3) | 3(10) | 1.97 | 0.964 | 0.812 | −0.127 | |
Student-student | 6(20) | 18(60) | 4(13.3) | 2(6.7) | 2.07 | 0.785 | 0.796 | 0.993 | |
Autonomy | 13(43.3) | 14(46.7) | 3(10) | 0(0) | 1.67 | 0.661 | 0.484 | −0.620 | |
Collaboration | 10(33.3) | 13(43.3) | 7(23.3) | 0(0) | 1.90 | 0.759 | 0.172 | −1.18 | |
Deepening | 15(50) | 9(30) | 4(13.3) | 2(6.7) | 1.77 | 0.935 | 1.04 | 0.223 | |
Resolution | 8(26.7) | 12(40) | 4(13.3) | 6(20) | 2.27 | 1.08 | 0.478 | −0.974 | |
Classtime | 17(56.7) | 8(26.7) | 3(10) | 2(6.7) | 1.67 | 0.922 | 1.30 | 0.897 | |
Ratingsa | 10(33.3) | 7(23.3) | 10(33.3) | 3(10) | 2.20 | 1.03 | 0.017 | −1.21 | |
Experimental group | Motivation | 2(6.9) | 9(31) | 9(31) | 9(31) | 2.86 | 0.953 | −0.239 | −0.987 |
Teacher-student | 5(17.2) | 5(17.2) | 15(51.7) | 4(13.8) | 2.62 | 0.942 | −0.513 | −0.534 | |
Student-content | 7(24.1) | 13(44.8) | 7(24.1) | 2(6.9) | 2.14 | 0.875 | 0.403 | −0.359 | |
Student-student | 5(17.2) | 10(34.5) | 11(37.9) | 3(10.3) | 2.41 | 0.907 | −0.033 | −0.698 | |
Autonomy | 6(20.7) | 8(27.6) | 6(20.7) | 9(31) | 2.62 | 1.14 | −0.089 | −1.42 | |
Collaboration | 3(10.3) | 8(27.6) | 8(27.6) | 10(34.5) | 2.86 | 1.03 | −0.346 | −1.05 | |
Deepening | 7(24.1) | 6(20.7) | 12(41.4) | 4(13.8) | 2.45 | 1.02 | −0.175 | −1.09 | |
Resolution | 4(13.8) | 6(20.7) | 12(41.4) | 7(24.1) | 2.76 | 0.988 | −0.431 | −0.703 | |
Class-time | 3(10.3) | 4(13.8) | 10(34.5) | 12(41.4) | 3.07 | 0.998 | −0.841 | −0.257 | |
Ratingsa | 16(20.7) | 5(17.2) | 11(37.9) | 7(24.1) | 2.66 | 1.07 | −0.341 | −1.09 |
Variables | µ(X1−X2) | tn1+n2-2 | df | d | rxy |
---|---|---|---|---|---|
Motivation | −0.595(2.27−2.86) | −2.321* | 57 | 0.058 | 0.294 |
Teacher-student | −0.487(2.13−2.62) | −2.123* | 57 | −0.114 | 0.271 |
Student-content | −0.171(1.97−2.14) | n.s. | 57 | 0.068 | 0.094 |
Student-student | −0.347(2.07−2.41) | n.s. | 57 | −0.085 | 0.204 |
Autonomy | −0.954(1.67−2.62) | −3.898** | 57 | 0.014 | 0.462 |
Collaboration | −0.962(1.90−2.86) | −4.085** | 57 | 0.025 | 0.478 |
Deepening | −0.682(1.77−2.45) | −2.676** | 57 | 0.060 | 0.334 |
Resolution | −0.492(2.27−2.76) | n.s. | 57 | −0.022 | 0.235 |
Class-time | −1.40(1.67−3.07) | −5.609** | 57 | 0.115 | 0.596 |
Ratingsa | −0.455(2.20−2.66) | n.s. | 57 | 0.021 | 0.214 |
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López Núñez, J.A.; López Belmonte, J.; Moreno Guerrero, A.J.; Pozo Sánchez, S. Effectiveness of Innovate Educational Practices with Flipped Learning and Remote Sensing in Earth and Environmental Sciences—An Exploratory Case Study. Remote Sens. 2020, 12, 897. https://doi.org/10.3390/rs12050897
López Núñez JA, López Belmonte J, Moreno Guerrero AJ, Pozo Sánchez S. Effectiveness of Innovate Educational Practices with Flipped Learning and Remote Sensing in Earth and Environmental Sciences—An Exploratory Case Study. Remote Sensing. 2020; 12(5):897. https://doi.org/10.3390/rs12050897
Chicago/Turabian StyleLópez Núñez, Juan Antonio, Jesús López Belmonte, Antonio José Moreno Guerrero, and Santiago Pozo Sánchez. 2020. "Effectiveness of Innovate Educational Practices with Flipped Learning and Remote Sensing in Earth and Environmental Sciences—An Exploratory Case Study" Remote Sensing 12, no. 5: 897. https://doi.org/10.3390/rs12050897
APA StyleLópez Núñez, J. A., López Belmonte, J., Moreno Guerrero, A. J., & Pozo Sánchez, S. (2020). Effectiveness of Innovate Educational Practices with Flipped Learning and Remote Sensing in Earth and Environmental Sciences—An Exploratory Case Study. Remote Sensing, 12(5), 897. https://doi.org/10.3390/rs12050897