scholarly journals Alter nation 2 4 , 2 (201 7 ) 34 - 55 34 Electronic ISSN: 2519 - 5476 ; DOI: https://doi.org/10.29086/2519 - 5476/2017/v24n2a3 ‘ Involvement ’ and ‘ Fun ’ as Potential for Deep Learning: Unusual Suspects in a Higher Education Economics Programme

2017 ◽  
Vol 24 (2) ◽  
pp. 34-55
Author(s):  
Jason Stratton Davis ◽  
Suriamurthee M. Maistry
Keyword(s):  
Higher Education ◽  
Deep Learning ◽  
Education Economics ◽  
Higher Education Economics

scholarly journals A critical appraisal of WinEcon and its use in a first-year undergraduate Economics programme

2011 ◽  
Vol 6 (3) ◽  
Author(s):  
D. J. Brooksbank ◽  
A. Clark ◽  
R. Hamilton ◽  
D. G. Pickernell
Keyword(s):  
Higher Education ◽  
Teaching And Learning ◽  
Learning Experience ◽  
Significant Feature ◽  
First Year ◽  
Learning Technology ◽  
Education Economics ◽  
Higher Education Economics ◽  
The Uk ◽  
Introductory Economics

WinEcon is a Windows-based introductory Economics CAL package designed for use in higher education. It is the product of the Economics Consortium of the TLTP (Teaching and Learning Technology Programme) consisting of eight university Economics departments. Each of these has been responsible for producing some of the 25 chapters (tutorials) of the finished product. Content is based on covering the common core of introductory Economics as revealed by a survey of higher-education Economics departments. WinEcon is provided, with an accompanying workbook, for a nominal registration fee in the UK. The package is important insofar as it is aimed at all first-year undergraduates studying Economics, which encompasses not only those taking straight Economics degrees but large numbers of students following introductory Economics as part of a Business Studies or Combined Studies course. With no competition to speak of, WinEcon is likely to become a significant feature of the learning experience of a large tranche of the undergraduate population, across a number of degree schemes. Indeed, for many of these students WinEcon will constitute their first major experience of CAL.DOI:10.1080/0968776980060307

Cross-national structure of economic competence: Insights from a German and Russian assessment

2022 ◽  
pp. 174549992110612
Author(s):  
Denis Federiakin ◽  
Olga Zlatkin-Troitschanskaia ◽  
Elena Kardanova ◽  
Carla Kühling-Thees ◽  
Jasmin Reichert-Schlax ◽  
...  
Keyword(s):  
Higher Education ◽  
General Factor ◽  
Education Economics ◽  
Education Students ◽  
Higher Education Economics ◽  
National Analysis ◽  
The University ◽  
Student Competencies ◽  
Economic Competence ◽  
Cross National

In this paper, we present a study, which models and measures the competencies of higher education students in business and economics—within and across countries. To measure student competencies in a valid and reliable way, the Test of Understanding in College Economics was used, which assesses microeconomic and macroeconomic competencies. The test was translated into several languages and adapted for different university contexts. In the presented study, the test contents were also compared with regard to the educational standards and the university curricula in Russia and Germany. Our findings from the cross-national analysis suggest one strong general factor of economic competence, which encompasses micro- and macroeconomic dimensions. This points to a stronger interconnection between learning and understanding economic contents than previous research suggests and indicates far-reaching curricular and instructional consequences for higher education economics as well as needs for further research, which are discussed in this paper.

Validity and fairness of a new entry diagnostics test in higher education economics

2020 ◽  
Vol 66 ◽  
pp. 100900
Author(s):  
Jasmin Schlax ◽  
Olga Zlatkin-Troitschanskaia ◽  
Roland Happ ◽  
Hans Anand Pant ◽  
Judith Jitomirski ◽  
...  
Keyword(s):  
Higher Education ◽  
Education Economics ◽  
Higher Education Economics ◽  
New Entry

Denoising of river surface photogrammetric DEMs using deep learning

2021 ◽  
Author(s):  
Radosław Szostak ◽  
Przemysław Wachniew ◽  
Mirosław Zimnoch ◽  
Paweł Ćwiąkała ◽  
Edyta Puniach ◽  
...  
Keyword(s):  
Higher Education ◽  
Deep Learning ◽  
Water Level ◽  
Water Surface ◽  
Research University ◽  
Static Characteristic ◽  
Water Levels ◽  
Training Dataset ◽  
Observation Data ◽  
Characteristic Points

<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>

Factors contributing to Gen Z's deep learning: investigating undergraduates' course experience in Malaysian private higher education institutions

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lei Mee Thien ◽  
Mi-Chelle Leong ◽  
Fei Ping Por
Keyword(s):  
Higher Education ◽  
Deep Learning ◽  
Significant Relationship ◽  
Teaching And Learning ◽  
Higher Education Institutions ◽  
Private Higher Education ◽  
Generic Skills ◽  
Content Type ◽  
Course Experience ◽  
Private Higher Education Institutions

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.

scholarly journals Gamification and deep learning approaches in higher education

2020 ◽  
pp. 100290
Author(s):  
Lidia Aguiar-Castillo ◽  
Alberto Clavijo-Rodriguez ◽  
Lidia Hernández-López ◽  
Petra De Saa-Pérez ◽  
Rafael Pérez-Jiménez
Keyword(s):  
Higher Education ◽  
Deep Learning ◽  
Learning Approaches
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