Growing demand for student-centered learning (SCL) has been observed in higher education settings including dentistry. However, application of SCL in dental education is limited. Hence, this study ...aimed to facilitate SCL application in dentistry utilising a decision tree machine learning (ML) technique to map dental students' preferred learning styles (LS) with suitable instructional strategies (IS) as a promising approach to develop an IS recommender tool for dental students.
A total of 255 dental students in Universiti Malaya completed the modified Index of Learning Styles (m-ILS) questionnaire containing 44 items which classified them into their respective LS. The collected data, referred to as dataset, was used in a decision tree supervised learning to automate the mapping of students' learning styles with the most suitable IS. The accuracy of the ML-empowered IS recommender tool was then evaluated.
The application of a decision tree model in the automation process of the mapping between LS (input) and IS (target output) was able to instantly generate the list of suitable instructional strategies for each dental student. The IS recommender tool demonstrated perfect precision and recall for overall model accuracy, suggesting a good sensitivity and specificity in mapping LS with IS.
The decision tree ML empowered IS recommender tool was proven to be accurate at matching dental students' learning styles with the relevant instructional strategies. This tool provides a workable path to planning student-centered lessons or modules that potentially will enhance the learning experience of the students.
•We proposed a specialized method that works well in assessing short summaries.•It integrates the semantic relations between words, and their syntactic composition.•We experimentally examine the ...influence of the combination of semantic and syntactic information on short summary assessment.•Experiments have displayed that it is to be preferred over the existing techniques.•We have developed the method as an intelligent tool to grade students’ summaries.
Summary writing is a process for creating a short version of a source text. It can be used as a measure of understanding. As grading students’ summaries is a very time-consuming task, computer-assisted assessment can help teachers perform the grading more effectively. Several techniques, such as BLEU, ROUGE, N-gram co-occurrence, Latent Semantic Analysis (LSA), LSA_Ngram and LSA_ERB, have been proposed to support the automatic assessment of students’ summaries. Since these techniques are more suitable for long texts, their performance is not satisfactory for the evaluation of short summaries. This paper proposes a specialized method that works well in assessing short summaries. Our proposed method integrates the semantic relations between words, and their syntactic composition. As a result, the proposed method is able to obtain high accuracy and improve the performance compared with the current techniques. Experiments have displayed that it is to be preferred over the existing techniques. A summary evaluation system based on the proposed method has also been developed.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Hadiths are important textual sources of law, tradition, and teaching in the Islamic world. Analyzing the unique linguistic features of Hadiths (e.g. ancient Arabic language and story-like text) ...results to compile and utilize specific natural language processing methods. In the literature, no study is solely focused on Hadith from artificial intelligence perspective, while many new developments have been overlooked and need to be highlighted. Therefore, this review analyze all academic journal and conference publications that using two main methods of artificial intelligence for Hadith text: Hadith classification and mining. All Hadith relevant methods and algorithms from the literature are discussed and analyzed in terms of functionality, simplicity, F-score and accuracy. Using various different Hadith datasets makes a direct comparison between the evaluation results impossible. Therefore, we have re-implemented and evaluated the methods using a single dataset (i.e. 3150 Hadiths from Sahih Al-Bukhari book). The result of evaluation on the classification method reveals that neural networks classify the Hadith with 94 % accuracy. This is because neural networks are capable of handling complex (high dimensional) input data. The Hadith mining method that combines vector space model, Cosine similarity, and enriched queries obtains the best accuracy result (i.e. 88 %) among other re-evaluated Hadith mining methods. The most important aspect in Hadith mining methods is query expansion since the query must be fitted to the Hadith lingo. The lack of knowledge based methods is evident in Hadith classification and mining approaches and this absence can be covered in future works using knowledge graphs.
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CEKLJ, EMUNI, FZAB, GEOZS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UL, UM, UPUK, VKSCE, ZAGLJ
Automatic Text Summarization (ATS) models yield outcomes with insufficient coverage of crucial details and poor degrees of novelty. The first issue resulted from the lengthy input, while the second ...problem resulted from the characteristics of the training dataset itself. This research employs the divide-and-conquer approach to address the first issue by breaking the lengthy input into smaller pieces to be summarized, followed by the conquest of the results in order to cover more significant details. For the second challenge, these chunks are summarized by models trained on datasets with higher novelty levels in order to produce more human-like and concise summaries with more novel words that do not appear in the input article. The results demonstrate an improvement in both coverage and novelty levels. Moreover, we defined a new metric to measure the novelty of the summary. Finally, we investigated the findings to discover whether the novelty is influenced more by the dataset itself, as in CNN/DM, or by the training model and its training objective, as in Pegasus.
The purpose of this study was to design and develop a theory-based summary writing online tool, named Summary Writing-Pal (SW-PAL), to assist English as a second language students in improving their ...summary writing. It also evaluates the effectiveness of SW-PAL in enhancing the students' summary writing performance and examines their perceptions about it. This mixed-method empirical study involved 53 English as a second language students majoring in computer science at a local university. Two intact groups were randomly chosen as the control and experimental groups with 26 and 27 students, respectively. The control group was taught using the conventional method, while the experimental group was taught using SW-PAL. Both groups were given a pre- and post-summary writing test. A Split-Plot Analysis of Covariance test was used to examine the effectiveness of the SW-PAL tool. A focus group interview was conducted to gather qualitative data on perceptions about the SW-PAL tool. Quantitative findings revealed that students' summary writing performance improved significantly due to the SW-PAL with a large effect size of .42. Qualitativewise, the users perceived SW-PAL to be useful as a motivating, challenging, and self-learning tool. Recommendations for practice for language instructors who wish to incorporate such a tool into their language instruction and suggestions for future research are discussed.
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NUK, OILJ, SAZU, UKNU, UL, UM, UPUK
•We proposed a specialized method for external plagiarism detection.•It integrates semantic and syntactic information to capture the meaning of passages.•It used SRL method to detect passive and ...active sentences.•It detects copied text, paraphrasing, transformation of sentences and changing of word.•Results displayed that it is to be preferred over PAN-11systems and other methods.
Plagiarism is the unauthorized use of the ideas, presentation of someone else's words or work as your own. This paper presents an External Plagiarism Detection System (EPDS), which employs a combination of the Semantic Role Labeling (SRL) technique, the semantic and syntactic information. Most of the available methods fail to capture the meaning in the comparison between a source document sentence and a suspicious document sentence when two sentences have same surface text. Therefore, it leads to incorrect or even unnecessary matching results. However, the proposed method is able to avoid selecting the source text sentence whose similarity with suspicious text sentence is high but its meaning is different. On the other hand, an author may change the sentence from: active to passive and vice versa; hence, the method also employed the SRL technique to tackle the aforementioned challenge. Furthermore, the method used the content word expansion approach to bridge the lexical gaps and identify the similar ideas that are expressed using different wording. The proposed method is able to detect different types of plagiarism such as the exact verbatim copying, paraphrasing, transformation of sentences, changing of word structure. As a result, the experimental results have displayed that the proposed method is able to improve the performance compared with the participating systems in PAN-PC-11 and other existing techniques.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
A sentiment analysis of Arabic texts is an important task in many commercial applications such as Twitter. This study introduces a multi-criteria method to empirically assess and rank classifiers for ...Arabic sentiment analysis. Prominent machine learning algorithms were deployed to build classification models for Arabic sentiment analysis classifiers. Moreover, an assessment of the top five machine learning classifiers’ performances measures was discussed to rank the performance of the classifier. We integrated the top five ranking methods with evaluation metrics of machine learning classifiers such as accuracy, recall, precision, F-measure, CPU Time, classification error, and area under the curve (AUC). The method was tested using Saudi Arabic product reviews to compare five popular classifiers. Our results suggest that deep learning and support vector machine (SVM) classifiers perform best with accuracy 85.25%, 82.30%; precision 85.30, 83.87%; recall 88.41%, 83.89; F-measure 86.81, 83.87%; classification error 14.75, 17.70; and AUC 0.93, 0.90, respectively. They outperform decision trees, K-nearest neighbours (K-NN), and Naïve Bayes classifiers.
This paper focuses on the design and evaluation of a theory‐based computer‐assisted summary writing learning environment called Summary Writing‐PAL (SW‐PAL). The SW‐PAL was developed based on four ...aspects: summarizing strategies, learning theories, prior knowledge, and cognitive load. A quasi‐experiment that involved 58 undergraduates majoring in Computer Science was conducted to examine the effectiveness of SW‐PAL in writing summaries. Two intact classes were selected with 28 and 30 students in control and experimental groups, respectively. The conventional teaching approach was employed in the control group, whereas the SW‐PAL was introduced to the experimental group. Pretest and posttest were administrated to both groups. The findings indicated that SW‐PAL improved students' summary writing performance. A significant variance was noted between intrinsic and extraneous cognitive load among students with varying levels of English proficiency in the experimental group, signifying that the SW‐PAL is more suitable for students with lower language proficiency.
Lay Description
What is already known about this topic:
Currently, there has been a lot of interest in CAL English language and numerous summary writing tools have been developed for language learning and teaching.
Existing summary writing tools did not focus on learning theories incorporation.
Worked examples approach is effective in well‐defined domain (mathematics, physics, etc.), how about if apply in ill‐defined domain (summary writing)?
What this paper adds:
Design and develop a CAL environment for summary writing.
Incorporate learning theories in CAL environment.
Apply worked example instructional approach in learning summary writing.
Implications for practice and/or policy:
Conventional teaching versus CAL environment in summary writing: CAL environment achieved better performance.
Worked examples in ill‐defined domain (summary writing) are also effective in language learning.
Worked examples is more effective for lower English language proficiency students.
Cognitive load: Lower language proficiency students demonstrated lower cognitive load when using CAL environment.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
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