Languaging dynamics in interactive lecturing: exploring an embodied approach to deep learning in L2 higher education contexts

2022 ◽  
pp. 1-20
Author(s):  
Dan Shi ◽  
Derek Irwin ◽  
Ping Du
2021 ◽  
Author(s):  
Radosław Szostak ◽  
Przemysław Wachniew ◽  
Mirosław Zimnoch ◽  
Paweł Ćwiąkała ◽  
Edyta Puniach ◽  
...  

<p>Unmanned Aerial Vehicles (UAVs) can be an excellent tool for environmental measurements due to their ability to reach inaccessible places and fast data acquisition over large areas. In particular drones may have a potential application in hydrology, as they can be used to create photogrammetric digital elevation models (DEM) of the terrain allowing to obtain high resolution spatial distribution of water level in the river to be fed into hydrological models. Nevertheless, photogrammetric algorithms generate distortions on the DEM at the water bodies. This is due to light penetration below the water surface and the lack of static characteristic points on water surface that can be distinguished by the photogrammetric algorithm. The correction of these disturbances could be achieved by applying deep learning methods. For this purpose, it is necessary to build a training dataset containing DEMs before and after water surfaces denoising. A method has been developed to prepare such a dataset. It is divided into several stages. In the first step a photogrammetric surveys and geodetic water level measurements are performed. The second one includes generation of DEMs and orthomosaics using photogrammetric software. Finally in the last one the interpolation of the measured water levels is done to obtain a plane of the water surface and apply it to the DEMs to correct the distortion. The resulting dataset was used to train deep learning model based on convolutional neural networks. The proposed method has been validated on observation data representing part of Kocinka river catchment located in the central Poland.</p><p>This research has been partly supported by the Ministry of Science and Higher Education Project “Initiative for Excellence – Research University” and Ministry of Science and Higher Education subsidy, project no. 16.16.220.842-B02 / 16.16.150.545.</p>


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lei Mee Thien ◽  
Mi-Chelle Leong ◽  
Fei Ping Por

PurposeThis study aims to examine the relationship between undergraduates' course experience and their deep learning approach and to identify areas of improvement to facilitate students' deep learning in the private higher education context.Design/methodology/approachData were collected from 844 Malaysian undergraduate students who studied in six private higher education institutions (HEIs) in Penang and Selangor. This study used partial least squares structural equation modelling (PLS-SEM) for data analysis.FindingsThe findings revealed that good teaching and appropriate assessment have no significant relationship with deep learning. Generic skills, clear goals and standards, appropriate workload and emphasis on independence are positively related to deep learning. Generic skills and emphasis on independence are two domains that deserve attention to enhance deep learning among undergraduates.Practical implicationsLecturers need to focus on to the cultivation of generic skills to facilitate students' deep learning. Student autonomy and student-centred teaching approaches should be empowered and prioritised in teaching and learning.Originality/valueThe current study has its originality in providing empirical findings to inform the significant relationship between dimensions of course experience and deep learning in Malaysian private HEIs. Besides, it also identifies the areas of improvement concerning teaching and learning at the private HEIs using importance-performance matrix analysis (IPMA) in a non-Western context.


Author(s):  
Lidia Aguiar-Castillo ◽  
Alberto Clavijo-Rodriguez ◽  
Lidia Hernández-López ◽  
Petra De Saa-Pérez ◽  
Rafael Pérez-Jiménez

2017 ◽  
Vol 6 (4) ◽  
pp. 362-367
Author(s):  
Brian D. Clocksin ◽  
Margo B. Greicar

Community engagement is commonly imbedded in the ethos of institutions of higher education and has been identified as a High Impact Practice for student learning and retention. The Sustained Engagement Experiences in Kinesiology (SEEK) program at the University of La Verne is a curriculum-wide approach that moves students through four stages of community engagement: Respect, Participating with Effort, Self-Directions, and Leadership. The stages are developmentally sequenced across the curriculum and provide opportunities for learners to move from passive participants to active engagement scholars. The engagement experiences serve to enhance students’ abilities to transfer what they learn in the classroom to real-life problems, foster an asset-based approach to community engagement, and facilitate a transition from surface-to deep-learning.


Author(s):  
Claire Gwinnett ◽  
John Cassella ◽  
Mike Allen

Multiple Choice Questions (MCQs) are a very well known, traditional and accepted method of assessment. The use of MCQs for testing students has produced numerous debates amongst academics concerning their effectiveness as they are viewed as practical and efficient but also perceived as possibly „too easy‟ and potentially unable to appropriately test the higher order cognitive skills that essay questions can assess.The use of MCQs in a forensic science context is currently being investigated, not only for use within forensic science education, but also for the testing of competency of qualified forensic practitioners. This paper describes a Higher Education Academy funded project that is investigating the design and the implementation of MCQs for testing forensic practitioners and the lessons that have been learnt so far, that will assist academics in the development of robust MCQ assessments within forensic science degrees to promote and assess deep learning.


Author(s):  
Mark Schofield

Gamification is a novel technology that can potentially motivate student learning. This chapter reflects on the implementation of a gamified application to support students' learning in terms of learning important facts concerning their study program. The scope of the chapter refers to two design features in which tests were conducted on the different configurations in a field experiment among UK university students. The initial feature identified was feedback, where it was anticipated that engagement would increase, with tailored feedback having a greater impact than generic feedback. The next feature identified was circumventing users from binge gaming through session limits, as this may potentially prevent deep learning. The findings suggest that tailored feedback was less effective than generic feedback, contradicting the initial anticipation. Session limits were found to not circumvent binging without a reduction in sessions. The findings suggest that gaming properties of gamified applications could impact sustaining and encouraging play.


2022 ◽  
pp. 1092-1106
Author(s):  
Mark Schofield

Gamification is a novel technology that can potentially motivate student learning. This chapter reflects on the implementation of a gamified application to support students' learning in terms of learning important facts concerning their study program. The scope of the chapter refers to two design features in which tests were conducted on the different configurations in a field experiment among UK university students. The initial feature identified was feedback, where it was anticipated that engagement would increase, with tailored feedback having a greater impact than generic feedback. The next feature identified was circumventing users from binge gaming through session limits, as this may potentially prevent deep learning. The findings suggest that tailored feedback was less effective than generic feedback, contradicting the initial anticipation. Session limits were found to not circumvent binging without a reduction in sessions. The findings suggest that gaming properties of gamified applications could impact sustaining and encouraging play.


Author(s):  
Ernest Mnkandla ◽  
Ansie Minnaar

<p class="3">The adoption of social media in e-learning signals the end of distance education as we know it in higher education. However, it appears to have very little impact on the way in which open and distance learning (ODL) institutions are functioning. Earlier research suggests that a significant part of the explanation for the slow uptake of social media in e-learning lies outside of conventional factors attributed to distance learning reforms.</p><p class="3">This research used the conceptual framework for online collaborative learning (OCL)<em> </em>in higher education. Social media such as blogs, wikis, Skype or Google Hangout, Facebook; and even mobile apps, such as WhatsApp; could facilitate deep learning and the creation of knowledge in e-learning at higher educational institutions.</p><p class="3">This metasynthesis is an interpretative integration of peer-reviewed qualitative research findings on social media in e-learning. It includes a synthesis of data, research methods, and theories used to investigate social media in e-learning. Seven themes emerged from the data which have been recrafted into a framework for social media in e-learning as the final product. The proposed framework could be useful to instructional designers and academics who are interested in using modern learning theories and want to adopt social media in e-learning in higher education as a deep learning strategy.</p>


2016 ◽  
Vol 65 (1) ◽  
pp. 32-39 ◽  
Author(s):  
Debbie Samuels-Peretz ◽  
Lana Dvorkin Camiel ◽  
Karen Teeley ◽  
Gouri Banerjee

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