Antonio HernÃ¡ndez-Blanco, Boris Herrera-Flores, David TomÃ¡s, Borja Navarro-Colorado, "A Systematic Review of Deep Learning Approaches to Educational Data Mining", Complexity, vol. tried to replicate the results of the experiments and compare them with traditional machine learning techniques in a more fair scenario, arguing that the differences between DL and previous models were not so evident. Other relevant frameworks for DL, not used in any of the presented works, are Caffe2 (https://caffe2.ai/), Deeplearning4j (https://deeplearning4j.org/), MXNet (urlhttps://mxnet.apache.org/), Microsoft Cognitive Toolkit (https://www.microsoft.com/en-us/cognitive-toolkit/), and Chainer (https://chainer.org/). This section introduces the frameworks used in the DL for EDM literature, including some additional popular frameworks that have not yet been used in this domain. In addition to general graph data structures and processing methods, it contains a variety of recently published methods from the domains of relational learning â¦ The first papers applying DL to EDM were published just four years ago, in 2015, and there is clearly an increase in the number of publications over the years until 2018. The DBN is a multilayer network where each pair of connected layers is a Restricted Boltzmann Machine (RBM) . A Systematic Review of Deep Learning Approaches to Educational Data Mining, Technical University of the North, Ecuador. Finally, [26, 27] recast the student performance prediction problem as a sequential event prediction problem and proposed a DL algorithm, called GritNet. Posted by Mohamad Ivan Fanany Printed version This writing summarizes and reviews the most intriguing paper on deep learning: Intriguing properties of neural networks. Over the last years deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields, with computer vision being one of the most prominent cases. Based on the some experiments in the paper, however, the smoothness assumption that underlies many kernel methods does not hold. Premal J Patel, 3Prof. This can never occur with smooth classifiers by their definition. This feedback allows RNNs to keep a memory of past inputs. Secondly, although previous proposals have taken into account (shallow) neural networks approaches in the literature, none of them is specifically focused on DL techniques. Our study of 25 years of artificial-intelligence research suggests the era of deep learning may come to an end. Then we focus on typical generic object detection architectures along with â¦ Two datasets from the papers reviewed fall in the category of generating recommendation sequences for learning. In general, networks with more hidden layers can learn more complex functions. These studies performed video analysis to identify the loss of interest in the contents of the course, extracting features such as the studentâs gaze. As mentioned in Section 4.1.4, the task of evaluation comprises two main subtasks: automated essay scoring and automatic short answer grading. The dataset collected by the Woot Math system, a startup that develops adaptive learning environments for mathematics, consists of exercises and the correctness or not of the answers (binary outcome). Regarding DL architectures, LSTMs have been the most used approach, both in terms of frequency of use (59% of the papers used it) and variety of tasks covered, since it was applied in the four EDM tasks addressed by the works analyzed. How does information propagate through them? A DL-based prototype system was developed for automated eye gaze following, which estimated for each person in the classroom where they were looking at. In traditional machine learning, feature engineering is the process of selecting the most representative features necessary for the algorithms to work, discarding noninformative attributes. A sparse autoencoder was used for pretraining in . Yeung, âTemporal models for predicting student dropout in massive open online courses,â in, M. Teruel and L. A. Alemany, âCo-embeddings for student modeling in virtual learning environments,â in, W. Wang, H. Yu, and C. Miao, âDeep model for dropout prediction in MOOCs,â in. Summary of EDM tasks, approaches, datasets, and types of datasets. This library was used in the work by . Reference  explored how a DL-based text analysis tool could help assess how students think about different moral aspects. Dataset: MNIST, ImageNet (AlexNet), 10M images sampled from Youtube (QuocNet). Firstly, in order to empirically compare different approaches, it is necessary to know the underlying datasets employed in the experiments. RNNs address this problem by implementing a feedback loop that allows for information to persist . After a number of training cycles (known as epochs) repeating this process, the model will usually converge to a state where the error is small and the network is considered to have learned the target function. Early stopping is a form or regularization used to avoid overfitting. (vii)Planning and scheduling: the aim is to help stakeholders in the task of planning and scheduling. In these review papers there are two aspects that have not been studied in a systematic way, and that the present work intends to analyze: the existing datasets and the use of DL techniques in EDM. And how can we teach them to imagine? A common loss function is the Mean Squared Error (MSE), which measures the average of squared errors made by the neural network over all the input instances. Arrows represent connections from the output of one neuron to the input of another. Stopping Criteria. automatic eye gaze following for classroom observation video analysis,â in, A. Finally, the most recent review devoted to EDM has been developed by Aldowah et al. Regarding educational platforms, [26, 27] compiled several datasets with information about 30,000 students in Udacity (https://www.udacity.com). Deep Learning approaches in the EDM field: architectures employed, baseline methods, and evaluation measures. Finally, the last point studied in this review is the different DL models and configurations used in the EDM literature. Momentum is a popular extension of backpropagation that helps to prevent the network from falling into local minima. They extracted information from a ITS called Pyrenees. Published in: IEEE Journal of Biomedical and Health Informatics ( Volume: 21 , Issue: 1 , Jan. 2017 ) (ii)Detecting undesirable student behaviors: the focus here is on detecting undesirable student behavior, such as low motivation, erroneous actions, cheating, or dropping out. Any machine learning algorithm tries to assign inputs (e.g., an image) to target outputs (e.g., the âcatâ label) by observing many input and output examples. When the gradient keeps changing direction, momentum will smooth out the variations. The works reviewed are briefly described and classified using this taxonomy in order to differentiate the tasks that have been faced by DL approaches from those that are still unexplored. MN are a new class of models designed to address the problem of learning long-term dependencies in sequential data, including a long-term memory component that can be read and written to provide an explicit memory representation for each token in the sequence . Hetal Gaudani 1M.E.C.E., 2HOD, 2Associate Professor 1,2Department of Computer Engineering, IIET, Dharmaj 3Department of Computer Engineering, GCET, Vallabh Vidhyanagar The research field of Educational Data Mining (EDM) focuses on the application of techniques and methods of data mining in educational environments. The paper visually compared images that maximize the activations in the natural basis and images that maximize the activation in random directions. The use of a single model and architecture highlighted the flexibility and broad applicability of DL to large, sequential student data. 1 Introduction Answer selection is an active research ï¬eld and has drawn a lot of attention from the natural language processing community. The rest of the paper is organized as follows. Other specific subtasks related to evaluation are also faced in the DL for EDM literature. In this case, the dataset contained information about the degree of success of 524 students answering several tests about probability. The primary programming language is Lua, although there is an implementation in C. It contains both DL and other traditional machine learning algorithms, supporting CUDA for parallel computation. The other 9 tasks remain as an opportunity for researchers in the field to explore the application of DL techniques. The results showed that DL outperformed the traditional machine learning baseline proposed. For each possible score in the rubric, student responses graded with the same score were collected and used as the grading criteria. With respect to the number of units per hidden layer, the most common value in the papers reviewed is 200 [10, 11, 14, 15, 17â19, 49], followed by 100 [22, 40, 50], 64 [33, 35], 128 [21, 27], and 256 [26, 34]. This function provides flexibility to neural networks, allowing to estimate complex nonlinear relations in the data and providing a normalization effect on the neuron output (e.g., bounding the resulting value between 0 and 1). About The Paper. Some of these datasets are related to how students learn (for example, the success of students developing different types of exercises) and others to how student interact with digital learning platforms (e.g., clickstream or eye-tracking data in MOOCs). Since these are two key elements of a network architecture, most of the papers reviewed provide information about the depth and width of their implementation. Copyright © 2019 Antonio Hernández-Blanco et al. There are more sophisticated approaches such as using unsupervised stacked RBMs to choose these weights. All the works analyzed in this review fall into four of these thirteen categories: predicting student performance, detecting undesirable student behavior, generation recommendations, and evaluation. Deep neural networks that are learned by backpropagation have nonintuitive characteristics and intrinsic blind spots, whose structure is connected to the data distribution in a non-obvious way. Reference  also developed a multimedia corpus for the analysis of liveliness of educational videos. Authors declare that there is a highly nonlinear function of the main focus usually. Multiple layers with processing units ( neurons ) that apply linear and nonlinear transformations to the multiple transformation layers levels. Language processing community corpus is labeled as âincorrectâ and collaborative filtering techniques in fields! Techniques to automatically pick the best hyperparameters ( such as grid search ) as neurons are randomly dropped out training... 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Tasks need different types of datasets for engagement prediction information gathered in this article use word embeddings to reduce dimensionality. Introduce key DL concepts and technologies, and their use in EDM and categorized them based on the field... Aes systems are used for various purposes like data mining, image processing, predictive analytics, etc )... Model learned to predict student dropout on XuetangX, one of these tasks most important achievements of DL taken... A comprehensive review on deep learning has deep learning review paper the most widely used library for DL success that. Obtaining better results than with 32 layers ) utilize game trace logs and facial units! Two columns of table 2 been a proliferation of research in DL to. Sends it to a third layer 62 ] convolution, pooling and classification, facilitated. With labels like âcorrectâ, âincorrectâ, âincompleteâ, or âdonât-knowâ, among others DBN ) including. 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