Most visible light communication (VLC) technologies use a light emitting diode (LED) as a data transmitter and a photodiode as a receiver. In this paper, we alternatively focus on the use of an image sensor or camera as a receiver due to its wide availability. However, the successful use of an image sensor mainly depends on the efficiency of the encoder-decoder and the modulation scheme. Thus, this paper proposes a novel modulation scheme based on a square wave signal called a square wave quadrature amplitude modulation (SW-QAM) method. This method can accommodate different camera settings and overcome the problem of LED flicker that is generally sensed by human eyes when the LED frequency is low. At the transmitter side, multiple LEDs can be used to increase the transmission bit rate, while, at the receiver side, a Wiener filter is used as a complementary technique to SW-QAM for solving the light interference phenomenon due to the closeness of one LED to another. Our experimental results show that the proposed SW-QAM scheme can decode symbols very well either the for close or far communication distances, dark or bright lighting conditions, and single or multiple LED points. INDEX TERMS Visible light communication (VLC), image sensor communication (ISC), exposure time, square wave quadrature amplitude modulation (SW-QAM).

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Learning management systems (LMSs) have been used massively due to the growing utilization of distance learning. This advancement has led to increased educational data that can be analyzed to improve the quality of the learning process. Learning analytics (LA) is one of the most important methods that can be used to analyze student performance. In this paper, we proposed an LA method based on deep learning, i.e., transformer encoder, to sequentially predict the student's final performance based on log activities provided by an LMS. The objective is to predict at-risk students of failing so that they can be mitigated as soon as possible. The proposed model was evaluated on the Open University LA Dataset (OULAD) for daily or weekly prediction. The results show that the model could predict at the early stage with an accuracy of 83.17% on withdrawn versus pass-distinction classes. Meanwhile, for other tasks, i.e., withdrawn-fail versus pass-distinction and fail versus pass-distinction tasks, the accuracy was at least 76% at the early stage. The proposed model was compared to the LSTM model. We found that the transformer encoder performed better than the LSTM, with the average difference values from 1% to 3% in terms of accuracy and from 3% to 7% in terms of F1-score for all tasks, based on the statistical testing. Furthermore, the ablation study using positional encoding, different feature aggregation methods, and weighted loss function for the imbalanced class problem was done. In OULAD, we found that model without positional encoding was better in all cases. In contrast, weekly feature aggregation and the use of the weighted loss function performed better only for some cases. INDEX TERMS Learning analytics, transformer encoder, student at-risk prediction, massive open online courses, sequential model, imbalanced dataset.

Pada saat ini, penyelenggaraan sistem pembelajaran daring menjadi hal yang penting di tengah pandemi untuk menekan persebaran virus COVID-19. Namun, sistem ini sangat sulit menjaga motivasi dan tingkat keterlibatan mahasiswa karena tidak ada interaksi langsung antara pengajar dengan mahasiswa. Makalah ini meninjau penggunaan data log mahasiswa untuk kebutuhan analisis pembelajaran guna memprediksi kinerja atau kecenderungan drop-out mahasiswa dari suatu mata kuliah dengan melihat pada data log interaksi mahasiswa dengan sistem dan data demografis mahasiswa menggunakan suatu data terbuka, yaitu Open University Learning Analytics Dataset (OULAD). Dari tinjauan beberapa artikel penelitian yang merujuk pada dataset tersebut, ada beberapa hal yang perlu ditinjau: 1) permasalahan yang sering diangkat, yaitu prediksi kecenderungan gagal dari mata kuliah tertentu, prediksi kinerja, dan prediksi keterlibatan mahasiswa; 2) fitur yang digunakan pada saat pemodelan, yaitu fitur demografis dan interaksi, baik yang diringkas secara harian atau mingguan dengan berbagai representasi fitur; 3) metode analisis pembelajaran yang secara khusus menggunakan metode pembelajaran mesin yang sering digunakan, yaitu Decision Tree (DT), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), dan Long Short-Term Memory (LSTM). Makalah ini juga mendiskusikan proses mitigasi dari mahasiswa yang berisiko, perancangan sistem data yang mendukung analisis pembelajaran, dan permasalahan yang sering ditemui pada saat proses pemodelan.

A non-iterative learning algorithm for artificial neural networks is an alternative to optimize the neural network parameters with extremely fast convergence time. Extreme learning machine (ELM) is one of the fastest learning algorithms based on a non-iterative method for a single hidden layer feedforward neural network (SLFN) model. ELM uses a randomization technique that requires a large number of hidden nodes to achieve the high accuracy. This leads to a large and complex model, which is slow at the inference time. Previously, we reported analytical incremental learning (AIL) algorithm, which is a compact model and a non-iterative deterministic learning algorithm, to be used as an alternative. However, AIL cannot grow its set of hidden nodes, due to the node saturation problem. Here, we describe a local sigmoid method (LSM) that is also a sufficiently compact model and a non-iterative deterministic learning algorithm to overcome both the ELM randomization and AIL node saturation problems. The LSM algorithm is based on ''divide and conquer'' method that divides the dataset into several subsets which are easier to optimize separately. Each subset can be associated with a local segment represented as a hidden node that preserves local information of the subset. This technique helps us to understand the function of each hidden node of the network built. Moreover, we can use such a technique to explain the function of hidden nodes learned by backpropagation, the iterative algorithm. Based on our experimental results, LSM is more accurate than other non-iterative learning algorithms and one of the most compact models.