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1.
PLoS One ; 15(8): e0236862, 2020.
Article in English | MEDLINE | ID: mdl-32857762

ABSTRACT

Language learning is an emerging research area where researchers have done significant contributions by incorporating technological assistantship (i.e., computer- and mobile-assistant learning). However, it has been revealed from the recent empirical studies that little attention is given on grammar learning with the proper instructional materials design and the motivational framework for designing an efficient mobile-assisted grammar learning tool. This paper hence, reports a preliminary study that investigated learner motivation when a mobile-assisted tool for tense learning was used. This study applied the Attention-Relevance-Confidence-Satisfaction (ARCS) model. It was hypothesized that with the use of the designed mobile- assisted tense learning tool students would be motivated to learn grammar (English tense). In addition, with the increase of motivation, performance outcome in paper- based test would also be improved. With the purpose to investigate the impact of the tool, a sequential mixed-method research design was employed with the use of three research instruments; Instructional Materials Motivation Survey (IMMS), a paper-based test and an interview protocol using a semi-structured interview. Participants were 115 undergraduate students, who were enrolled in a remedial English course. The findings showed that with the effective design of instructional materials, students were motivated to learn grammar, where they were positive at improving their attitude towards learning (male 86%, female 80%). The IMMS findings revealed that students' motivation increased after using the tool. Moreover, students improved their performance level that was revealed from the outcome of paper-based instrument. Therefore, it is confirmed that the study contributed to designing an effective multimedia based instructions for a mobile-assisted tool that increased learners' motivational attitude which resulted in an improved learning performance.


Subject(s)
Educational Measurement/methods , Learning , Motivation , Attention , Female , Humans , Interviews as Topic , Language , Male , Mobile Applications , Personal Satisfaction , Self Concept , Students/psychology , Young Adult
2.
PLoS One ; 13(5): e0196957, 2018.
Article in English | MEDLINE | ID: mdl-29715321

ABSTRACT

[This corrects the article DOI: 10.1371/journal.pone.0179703.].

3.
PLoS One ; 13(1): e0179703, 2018.
Article in English | MEDLINE | ID: mdl-29351287

ABSTRACT

Designing an efficient association rule mining (ARM) algorithm for multilevel knowledge-based transactional databases that is appropriate for real-world deployments is of paramount concern. However, dynamic decision making that needs to modify the threshold either to minimize or maximize the output knowledge certainly necessitates the extant state-of-the-art algorithms to rescan the entire database. Subsequently, the process incurs heavy computation cost and is not feasible for real-time applications. The paper addresses efficiently the problem of threshold dynamic updation for a given purpose. The paper contributes by presenting a novel ARM approach that creates an intermediate itemset and applies a threshold to extract categorical frequent itemsets with diverse threshold values. Thus, improving the overall efficiency as we no longer needs to scan the whole database. After the entire itemset is built, we are able to obtain real support without the need of rebuilding the itemset (e.g. Itemset list is intersected to obtain the actual support). Moreover, the algorithm supports to extract many frequent itemsets according to a pre-determined minimum support with an independent purpose. Additionally, the experimental results of our proposed approach demonstrate the capability to be deployed in any mining system in a fully parallel mode; consequently, increasing the efficiency of the real-time association rules discovery process. The proposed approach outperforms the extant state-of-the-art and shows promising results that reduce computation cost, increase accuracy, and produce all possible itemsets.


Subject(s)
Data Mining/methods , Datasets as Topic , Algorithms , Databases, Factual
4.
Sensors (Basel) ; 17(3)2017 Mar 13.
Article in English | MEDLINE | ID: mdl-28335399

ABSTRACT

The use of the eddy current technique (ECT) for the non-destructive testing of conducting materials has become increasingly important in the past few years. The use of the non-destructive ECT plays a key role in the ensuring the safety and integrity of the large industrial structures such as oil and gas pipelines. This paper introduce a novel ECT probe design integrated with the distributed ECT inspection system (DSECT) use for crack inspection on inner ferromagnetic pipes. The system consists of an array of giant magneto-resistive (GMR) sensors, a pneumatic system, a rotating magnetic field excitation source and a host PC acting as the data analysis center. Probe design parameters, namely probe diameter, an excitation coil and the number of GMR sensors in the array sensor is optimized using numerical optimization based on the desirability approach. The main benefits of DSECT can be seen in terms of its modularity and flexibility for the use of different types of magnetic transducers/sensors, and signals of a different nature with either digital or analog outputs, making it suited for the ECT probe design using an array of GMR magnetic sensors. A real-time application of the DSECT distributed system for ECT inspection can be exploited for the inspection of 70 mm carbon steel pipe. In order to predict the axial and circumference defect detection, a mathematical model is developed based on the technique known as response surface methodology (RSM). The inspection results of a carbon steel pipe sample with artificial defects indicate that the system design is highly efficient.

5.
Sensors (Basel) ; 16(3): 298, 2016 Feb 26.
Article in English | MEDLINE | ID: mdl-26927123

ABSTRACT

Non-destructive eddy current testing (ECT) is widely used to examine structural defects in ferromagnetic pipe in the oil and gas industry. Implementation of giant magnetoresistance (GMR) sensors as magnetic field sensors to detect the changes of magnetic field continuity have increased the sensitivity of eddy current techniques in detecting the material defect profile. However, not many researchers have described in detail the structure and issues of GMR sensors and their application in eddy current techniques for nondestructive testing. This paper will describe the implementation of GMR sensors in non-destructive testing eddy current testing. The first part of this paper will describe the structure and principles of GMR sensors. The second part outlines the principles and types of eddy current testing probe that have been studied and developed by previous researchers. The influence of various parameters on the GMR measurement and a factor affecting in eddy current testing will be described in detail in the third part of this paper. Finally, this paper will discuss the limitations of coil probe and compensation techniques that researchers have applied in eddy current testing probes. A comprehensive review of previous studies on the application of GMR sensors in non-destructive eddy current testing also be given at the end of this paper.

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