Students’ Feedback of mDPBL Approach and the Learning Impact towards Computer Networks Teaching and Learning

This study presents students’ feedback and learning impact on design and development of a multimedia learning in Direct Problem-Based Learning approach (mDPBL) for Computer Networks in Dian Nuswantoro University, Indonesia. This study examined the usefulness, contents and navigation of the multimedia learning as well as learning impacts towards mDPBL approach which used. A total of 276 students who took Computer Networks subject from two different departments participated in this study of a quasi-experiment in year 2016. Two different ways of teaching, one the normal of teaching/traditional approach, another one using mDPBL approach of teaching. However, the purpose of this study, Analysis and report in this study only the feedback of the students participated in mDPBL group (n=136). Nearly all students have positive feedback of the multimedia learning especially usefulness, contents and navigation, and also they have positive feedback of the mDPBL teaching approach. While, the overall feedback towards mDPBL approach presented in the findings/results section

Proposed object-based e-learning framework embracing cloud computing

E-learning is continually evolved in the adaptation of emerging technologies and pedagogies, so does the development of learning objects. Object-based learning approach has been widely adopted in e-learning.The principle concern in the conventional e-learning models is the inflexibility of the e-learning content to assist the use and management of the learning sources which are highly distributed.Rigidity of e-learning content limits its reusability and shareability.Possessing the ability to deliver strong computing power and secure data storage as services, Cloud computing is a promising computing model to promote innovative changes and add notable values to e-learning landscape.The flexibility and on-demand access to a centralized shared pool of computing resources provided by Cloud computing enables high re-usability and share-ability, hence overcomes the principle concern in e-learning due to the rigidity of e-learning content.This paper describes the design of an object-based e-learning framework embracing Cloud computing.The proposed object-based e learning framework can be used to form a new education domain that shares the Cloud characteristics of elasticity, flexibility, efficiency and reliability.Principal to the framework design is the development of Cloud-based e-learning objects where learners have the flexibility to access, personalize and deploy them in e-learning environment.Cloud-based e-learning objects are highly adaptable, reusable and easy changeable, hence allowing them to be used dynamically with greater customizability and flexibility in e-learning.Adapting Cloud computing into object-based e-learning is believed to be able to create a breakthrough in future dynamic -e-learning development

The impacts of IT capabilities on teaching and learning mathematics: A conceptual framework for Malaysia

The steep fall of mathematics performance of Malaysian students in International Mathematics & Science Study (TIMSS) 2011 signaling urgent needs to ensure effective teaching and learning in mathematics. The insufficiency of Information Technology Capabilities (ITCs) is a major challenge for teaching and learning mathematics in Malaysia. The key objective of this research is to propose a comprehensive framework, which incorporates crucial ITCs such as dynamic, integrating, data management and collaborating capabilities for Malaysian schools to improve mathematics performance. Pilot and actual surveys will be carried out among teachers and students to design the appropriate ITC framework for teaching and learning mathematics. Based on the survey data, an applicable model for representing and reasoning about ITC in improving mathematics performance will be designed from learning Bayesian networks

Defining Knowledge Management (KM) activities from Information Communication Technologies (ICTs) perspective

KM practitioners or managers may sometimes face difficultities when they come to adop definitions to plan for effective KM and information infrastructure in their respective situations to achieve organisational competitive advanatge (CA). This paper is to review and examine the variations and similarities from the various definitions of KM activities since 1990s from the perspective of Information Communication Technologies (ICTs) with the aim of finding out which is the most suitable one to adopt. A keyword index search of ‘knowledge management’ was conducted on 01 December 2009 in the ProQuest Central online database. 25932 articles were found. After topic filtering, there were only 254 articles related to the keyword and 55 of them were connected to the ‘knowledge management activities’. Based on the scope of the 55 articles, this paper identified that there are four KM activities: creating, storing, sharing and utilising knowledge

Relationship between information infrastructure capability and organisational competitive advantage: An empirical study on MSC Malaysia companies

The progressive movement of globalisation indicates that developing economies, especially Malaysia, need enhanced and new level of labour force that can work effectively across national and cultural boundaries to achieve competitive advantage. Among the requirements considered critical to achieving competitive advantage is investment in appropriate information and communications technology (ICT) applications, which could enable information infrastructure capability (IIC). However, managers always face with a lot of difficulties to obtain the appropriate IIC due to what is considered an elusive empirical link between IIC and competitive advantage. This research argues that IIC, if examined carefully and in a holistic fashion, would be able to create, store, share and use knowledge for competitive advantage. This research also addressed indirect relationships between IIC and competitive advantage. The conceptual model for this research comprises competitive advantage as the dependent variable and the dimensions of IIC namely, dynamic capability, integrating capability, data management capability, security capability, utility capability and collaborating capability, corroborated from past studies, as the independent variables. The capabilities, which were distinct but highly interrelated, constrain, facilitate, and reinforce each other. The conceptual framework of this research was underpinned by Organisational Information Processing Theory and empirically tested using a set of survey questionnaire, which was validated and reliability tested

Analysis of knowledge management processes for human capital of Malaysian plantation industry

Agriculture industry which is the important economy sector contributes to Malaysian economic growth, like many other industries continues to face multiple management challenges such as "talent shortage" and "brain drain" in this globalisation era. Such challenges faced by industry among others are due to failure of the industry in implementing knowledge management (KM) effectively and efficiently. In view of this, a better understanding of KM processes across the agriculture industry is imperative for the competitive advantage of the industry

Achieving Competitive Advantage (CA) through Information Infrastructure Capability (IIC): An empirical justification

The goals of most of the organisations are achieving their sustainable competitive advantage (CA) challenges. One of the organizational initiatives is to invest appropriate information infrastructures. However, organisations may face difficulties to select effective information infrastructures in their respective situations to achieve CA from elusive information. This paper is to connect the Information infrastructure capability (IIC) to CA with empirical justification. Hence, IICs are categorised into dynamic capability (D), integrating capability (I), data management capability (DM), security capability (S), utility capability (U) and collaborating capability (C) from past studies. This paper then empirically test the model using a set of survey data collected from 295 MSC Malaysia companies with the aim to analyse IICs in a holistic way. Four capabilities emerge from the factor analysis as IICs: D, I, DM and U. These results show there is an empirical link between IICs which comprises D, I, DM and U with CA. Finally, a clear full chain of variables model connecting IIC to organisational CA is obtained to fill the research lacuna

Information Technology Capability (ITC) Framework to Improve Learning Experience and Academic Achievement of Mathematics in Malaysia

Poor mathematics performance was generally reported from international assessments such as Programme for International Student Assessment (PISA) and Trends in International Mathematics and Science Study (TIMSS) among Malaysian students. Malaysia is ranked 52nd and 48th in the assessments for 2012 and 2018 respectively, while Singapore, Japan, South Korea, and even Vietnam have consistently performed well and held the top spots among the 78 countries evaluated in the PISA. Although numerous new technologies have been introduced, developed and implemented for education, incorporation of IT capability (ITC) to teach and learn Mathematics where still lacking commonly. Additionally, learning Mathematics in the traditional teaching contents could not accomplish desired learning outcomes because of dry contents and dull teachers. Therefore, this study is to design an appropriate ITC framework for improving learning experience and academic achievement of learning Mathematics. This study has adopted the development model of Analysis, Design, Development, Implementation and Evaluation (ADDIE) and Mayer (2010)’s cognitive theory for multimedia instructional content design. This study developed a new Multimedia Probability and Statistics system (MMPASS) for a subject of Probability and Statistics. The developed topics were concepts of discrete random variables and probability distribution function which were puzzled by students from preliminary study. An experiment was conducted with both control and experimental groups. The developed MMPASS blended multiple influential multimedia elements in the learning contents. A quantitative method and proportional stratified sampling were used to collect data. The blended topics were used by the experimental group whilst the control group was solely learning using the existing learning contents. Questionnaires were distributed to both groups after the lessons. 66 students participated in this survey. The collected data was then analysed and an ITC model was formed. Results of this study show that Perceived System Quality, Perceived Information Content Quality and Perceived System Performance as independent variables significantly improved learning experience. The findings also reveal that the performances of the experimental group have a higher mean score (9.65/10.00) compared to the control group (8.03/10.00), indicating the use of MMPASS improved students’ learning performance in subjects that involve understanding of concepts. While there is a lack of established ITC framework and IT application for Mathematics education in Malaysia, this study has verified the use of ITC improving performance of learning Mathematics in Malaysia

Evaluation of Cryptocurrency Price Prediction Using LSTM and CNNs Models

Cryptocurrencies created by Nakamoto in 2009 have gained significant interest due to their potential for high returns. However, the cryptocurrency market's unpredictability makes it challenging to forecast prices accurately. To tackle this issue, a deep learning model has been developed that utilizes Long Short-Term Memory (LSTM) neural networks and Convolutional Neural Networks (CNNs) to predict cryptocurrency prices. LSTMs, a type of recurrent neural network, are well-suited for analyzing time series data and have been successful in various prediction applications. Additionally, CNNs, primarily used for image analysis tasks, can be employed to extract relevant patterns and characteristics from input data in Bitcoin price prediction applications. This study contributes to the existing related works on cryptocurrency price prediction by exploring various predictive models and techniques, which involve a machine learning model, deep learning model, time series analysis, and as well as a hybrid model that combines deep learning methods to predict cryptocurrency prices as well as enhance the accuracy and reliability of the price predictions. To ensure accurate predictions in this study, a trustworthy dataset from investing.com was sought. The dataset, sourced from investing.com, consists of 1826 time series data samples. The dataset covers the time frame from January 1, 2018, to December 31, 2022, providing data for a period of 5 years. Subsequently, pre-processing was conducted on the dataset to guarantee the quality of the input. As a result of absent values and concerns regarding the dataset's obsolescence, an alternative dataset was sourced to avoid these issues. The performance of the LSTM and CNN models was evaluated using root mean squared error (RMSE), mean squared error (MSE), mean absolute error (MAE) and R-squared (R2 ). It was observed that they outperformed each other to a certain degree in short-term forecasts compared to long-term predictions, where the R2 values for LSTM range from 0.973 to 0.986, while for CNNs, they range from 0.972 to 0.988 for 1 day, 3 days and 7 days windows length. Nevertheless, the LSTM model demonstrated the most favorable performance with the lowest error rate. The RMSE values for the LSTM model ranged from 1203.97 to 1645.36, whereas the RMSE values for the CNNs model ranged from 1107.77 to 1670.93. As a result, the LSTM model exhibited a lower error rate in RMSE and achieved the highest accuracy in R2 compared to the CNNs model. Considering these comparative outcomes, the LSTM model can be deemed as the most suitable model for this specific case