Indriana Hidayah

Learning style detection is essential for developing adaptive learning tailored to learner preferences. Extensive research has been conducted to address the limitations of direct learner engagement, behavioral-based approaches, and the lack of learner-teacher interaction caused by the shift to online learning. Many learning style models have been widely used and evaluated for their performance in supporting adaptive learning. However, implementing Multiple Intelligence Learning Styles (MILS) remains limited in online learning due to low detection performance. This study employs machine learning approaches to enhance the detection performance of Multiple Intelligence Learning Styles. This study proposes an integrated machine learning framework to enhance MILS classification by combining data preprocessing, log-modulus transformation, and class imbalance handling via Synthetic Minority Over-Sampling Technique (SMOTE), Adaptive Synthetic Sampling Approach (ADASYN), and SMOTE-Tomek. Recursive Feature Elimination (RFE) with Support Vector Machine (SVM) is used for feature selection while eight machine learning models are used for classification. Interactions between learners and the Learning Management System (LMS) comprise the dataset, which are labeled using the Multiple Intelligence Inventory. The SVM model using ADASYN outperforms previous studies and other models, achieving the highest F1-Score of 89%, according to experimental data using 5-fold cross-validation. Statistical tests (Shapiro-Wilk, Analysis of variance (ANOVA), and paired t-tests) confirm significant differences in performance between models. The proposed approach demonstrates enhanced detection performance and can be adapted to broader e-learning applications, supporting adaptive and personalized learning systems.

Nielsen’s heuristics are widely recognized for usability evaluation, but they are often considered insufficiently specific for assessing particular domains, such as e-learning. Currently, e-learning plays a pivotal role in higher education because of the shift in the educational paradigm from a teacher-centered approach to a student-centered approach. The criteria utilized in multiple sets of heuristics for evaluating e-learning are carefully examined based on the definitions of each criterion. If there are similarities in meaning among these criteria, they are consolidated into a single criterion, resulting in the creation of 20 new criteria (spanning three primary aspects) for the evaluation of e-learning. These 20 new criteria encompass key aspects related to the user interface, learning development, and motivation. Each aspect is assigned a weight to facilitate prioritization when implementing improvements to evaluate e-learning, which is especially beneficial for institutions with limited resources responsible for the relevant units. In terms of weighting, there is room for enhancement to attain more optimal weighting outcomes by employing a Fuzzy Preference Programming method known as Inverse Trigonometric Fuzzy Preference Programming (ITFPP). The higher the assigned weight, the greater the priority for implementing improvements.

The impact of the COVID-19 pandemic influences different aspects of society, including tourism. The policy of limiting community activities encourages the closu The impact of the COVID-19 pandemic influences different aspects of society, including tourism. The policy of limiting community activities encourages the closu

Automatic short answer scoring methods have been developed with various algorithms over the decades. In the Indonesian language, the string-based similarity is more commonly used. This method is difficult to accurately measure the similarity of two sentences with significantly different word lengths. This problem has been handled by the Geometric Average Normalized-Longest Common Subsequence (GAN-LCS) method by eliminating non-contributive words utilizing the Longest Common Subsequence method. However, students’ answers may vary not only in character length but also in the words they choose. For instance, some students tend only to write the abbreviations or acronyms of the phrase instead of writing meaningful words. As a result, it will reduce the intersection character between the reference answer and the student answer. Moreover, it can change the sentence structure even though it has the same meaning by definition. Therefore, this study aims to improve GAN-LCS method performance by incorporating the abbreviation checker to handle the abbreviations or acronyms found in the reference answer or student answer. The dataset used in this study consisted of 10 questions with 1 reference answer for each question and 585 student answers. The experimental results show an improvement in GAN-LCS performance that could run 34.43% faster. Meanwhile, the Root Mean Square Error (RSME) value became lower by 7.65% and the correlation value was increased by 8%. Looking forward, future studies may continue to investigate a method for automatically generate the abbreviations dictionary.

Globalization can cause several problems and pressures of mind for both individuals and groups. Various kinds of problems can certainly lead to psychological disorders, one of which is depression. Indonesia itself is one of the countries with a high number of people with depressive disorders. Depressive disorder itself can have many consequences ranging from lack of enthusiasm to even death. Facing these serious problems, the government should be able to address the mental disorder that is currently happening. However, the reality is still far from this. Inadequate infrastructure, equality problems for each region, and shortages of experts are the main problems at this time. The expert system is considered to be a solution in solving these problems. Web-based expert systems can replace the role of experts in the process of initial diagnosis of depressive disorders, patients can also access them easily. The calculation method implemented is the Certainty Factor method. This method is considered suitable in the diagnosis of depression. The implementation of the CF method in the diagnosis of depression can provide a confidence level of up to 94.9%. The expert system is expected to be able to eliminate human errors, speed up the diagnostic process, make it easier for health workers, and provide standards for related parties in handling mental disorders

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One of the well-known recommendation systems is memory-based collaborative filtering that utilizes similarity metrics. Recently, the similarity metrics have taken into account the user rating and user behavior scores. The user behavior score indicates the user preference in each product type (genre). The added user behavior score to the similarity metric results in more complex computation. To reduce the complex computation, we combined the clustering method and user behavior score-based similarity. The clustering method applies k-means clustering by determination of the number of clusters using the Silhouette Coefficient. Whereas the user behavior score-based similarity utilizes User Profile Correlation-based Similarity (UPCSim). The experimental results with the MovieLens 100k dataset showed a faster computation time of 4.16 s. In addition, the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) values decreased by 1.88% and 1.46% compared to the baseline algorithm.

Modeling students' knowledge is a fundamental part of online learning platforms. Knowledge tracing is an application of student modeling which renowned for its ability to trace students' knowledge. Knowledge tracing ability can be used in online learning platforms for predicting learning performance and providing adaptive learning. Due to the wide uses of knowledge tracing in student modeling, this study aims to understand the state-of-the-art and future research of knowledge tracing. This study focused on reviewing 24 studies published between 2017 to the third quarter of 2021 in four digital databases. The selected studies have been filtered using inclusion and exclusion criteria. Several previous studies have shown that there are two approaches used in knowledge tracing, including probabilistic and deep learning. Bayesian Knowledge Tracing model is the most widely used in the probabilistic approach, while the Deep Knowledge Tracing model is the most popular model in the deep learning approach. Meanwhile, ASSISTments 2009–2010 is the most frequently tested dataset for probabilistic and deep learning approaches. In the future, additional studies are required to explore several models which have been developed previously. Therefore this study provides direction for future research of each existing approach.

Makalah ini bertujuan untuk memperbaiki kinerja sistem penilaian tes uraian singkat. Perbaikan kinerja tersebut dilakukan dengan menambahkan regresi linear sederhana pada keluaran gabungan metode cosine similarity (dengan pembobotan frekuensi kata berbasis metode Term Frequency-Inverse Document Frequency (TF-IDF)) dan mekanisme pencocokan kata. Regresi linear dilakukan dengan menjadikan nilai uraian singkat (hasil cosine similarity dan pencocokan kata) sebagai variabel regressor. Untuk mengetahui efektivitas sistem penilaian yang diusulkan, diukur kinerja sistem penilaian relatif terhadap nilai manual yang dilakukan oleh dosen. Diperoleh bahwa sebelum dilakukan regresi linear, sistem penilaian cenderung mengeluarkan nilai lebih tinggi (nilai mengalami bias) dibandingkan nilai manual yang dilakukan dosen. Regresi linear memperbaiki kinerja sistem penilaian tersebut dengan mengurangi bias penilaian secara signifikan, yaitu nilai yang diberikan tidak cenderung lebih tinggi maupun lebih rendah daripada nilai manual oleh dosen. Bahwa bias penilaian dapat diturunkan secara signifikan dengan metode yang sederhana, yaitu regresi linear, diharapkan dapat memberikan kontribusi terhadap akselerasi proses penerapan sistem penilaian otomatis untuk tes uraian pada teknologi pembelajaran dalam jaringan seperti e-learning.