The Management of Digital Transformation in Higher Education:An Assessment Proposal

The impacts of the current digital era are growing more and more on the way in which higher education(HE)offers learning programs at every level.Smart learning(SL)represents the evolution of educational approaches and techniques that capitalizes on all the opportunities deriving from new digital ecosystems.This study argues that the usefulness of HE can benefit from rethinking the traditional active learning(AL)model to smart ones.Thus,this research aims at investigating how to adapt the performance evaluation when the operations of HE turn to digitalized models.Therefore,this paper designs a research approach that allows accounting for the effects that the adoption of some SL strategies and tools has on the engagement of students and the aggregate performance of HE programs that adopt AL.The results of this study would help academics and HE managers assess the effectiveness of SL initiatives they plan to adopt.

Smart Education Form under Digital Transformation in Education:Key Features and Generating Ways

The high-quality development of education and a strong education system must be materialized through digital transformation in education.Smart education is the significant measure of this transformation that enjoys key features like cultivating students foremost,scenario perception,data-driven approach,and man-machine collaboration.Amid digital transformation in education,smart education can be generated in two approaches.One involves creating a smart learning environment through constructing an education private network,an education big data center,an integrated education cloud platform,and a smart campus.The other one is to empower core application scenarios like constructing educational resources,innovating teaching modes,reforming the educational evaluation system,improving the information competence of educators and students,and enabling intelligent education governance,which constitutes a basis for redesigning the educational process to support a high-quality education system.

Image classification of lotus in Nong Han Chaloem Phrakiat Lotus Park using convolutional neural networks

The Nong Han Chaloem Phrakiat Lotus Park is a tourist attraction and a source of learning regarding lotus plants.However,as a training area,it lacks appeal and learning motivation due to its conventional presentation of information regarding lotus plants.The current study introduced the concept of smart learning in this setting to increase interest and motivation for learning.Convolutional neural networks(CNNs)were used for the classification of lotus plant species,for use in the development of a mobile application to display details about each species.The scope of the study was to classify 11 species of lotus plants using the proposed CNN model based on different techniques(augmentation,dropout,and L2)and hyper parameters(dropout and epoch number).The expected outcome was to obtain a high-performance CNN model with reduced total parameters compared to using three different pre-trained CNN models(Inception V3,VGG16,and VGG19)as benchmarks.The performance of the model was presented in terms of accuracy,F1-score,precision,and recall values.The results showed that the CNN model with the augmentation,dropout,and L2 techniques at a dropout value of 0.4 and an epoch number of 30 provided the highest testing accuracy of 0.9954.The best proposed model was more accurate than the pre-trained CNN models,especially compared to Inception V3.In addition,the number of total parameters was reduced by approximately 1.80–2.19 times.These findings demonstrated that the proposed model with a small number of total parameters had a satisfactory degree of classification accuracy.

Enhanced Learning Resource Recommendation Based on Online Learning Style Model

Smart learning systems provide relevant learning resources as a personalized bespoke package for learners based on their pedagogical needs and individual preferences.This paper introduces a learning style model to represent features of online learners.It also presents an enhanced recommendation method named Adaptive Recommendation based on Online Learning Style(AROLS),which implements learning resource adaptation by mining learners’behavioral data.First,AROLS creates learner clusters according to their online learning styles.Second,it applies Collaborative Filtering(CF)and association rule mining to extract the preferences and behavioral patterns of each cluster.Finally,it generates a personalized recommendation set of variable size.A real-world dataset is employed for some experiments.Results show that our online learning style model is conducive to the learners’data mining,and AROLS evidently outperforms the traditional CF method.