Several tasks can be added to the list of tasks previously mentioned for RNNs: text generation [81], question answering [82] and action recognition in video sequences [83], among others. Table 3 summarizes the works in EDM studied in this article (first column), the architectures implemented (second column), the baseline methods employed (third column), the evaluation measures used to compare DL approaches and baseline methods (fourth), and the performance achieved by DL methods in that comparison (fifth). The main advantage of using machine learning is that, once an algorithm learns what to do with data, it can do its work automatically. In addition to image processing [70], this type of networks has been applied to video recognition [71], game playing [72], and different natural language processing tasks [73]. Even though it is stated that such adversarial images in reality are rarely observed, it is challenging to propose algorithms that can effectively handle the adversarial examples. The answers were manually evaluated by experts with labels like “correct”, “incorrect”, “incomplete”, or “don’t-know”, among others. Momentum is a popular extension of backpropagation that helps to prevent the network from falling into local minima. The number of architectures and algorithms that are used in DL is wide and varied. Review articles are excluded from this waiver policy. It was used by [36] for automatic eye gaze following in the classroom. The recent striking success of deep neural networks in machine learning raises profound questions about the theoretical principles underlying their success. A group of Harrisburg University professors and a PhD student developed automated computer facial recognition software capable of predicting whether someone is likely going to be a criminal. An open challenge for future research is the recommendation of learning resources in an informal setting. Other configurations include 5 [31], 15 [44], 20 [28], 40 [37], and 300 [20]. This paper is very enlighting for two reasons: (1) Two images that we see as similar are actually can be interpreted as totally different images (objects), and vice versa, two images that we see as different are actually can be interpretated as the same; (2) The deep NN still does not see as human sees. In order for the network to learn, it is necessary to find the weights of each layer that provides the best mapping between the input examples and the corresponding objective outputs. Van Merriënboer, C. Gulcehre et al., “Learning phrase representations using RNN encoder-decoder for statistical machine translation,” in, M. C. Mozer, “A focused backpropagation algorithm for temporal pattern recognition,” in, S. Hochreiter and J. Schmidhuber, “Long short-term memory,”, K. Cho, B. van Merrienboer, D. Bahdanau, and Y. Bengio, “On the properties of neural machine translation: Encoder-decoder approaches,” in. It was the most widely used library for DL before the arrival of other competitors such as Tensorflow, Caffe, and PyTorch. Creating courseware: the purpose is to help educators to automatically create and development course materials using students' usage information. Each node calculates the sum of the products of the weights and the inputs. The inspection of individual units makes the implicit assumption that the units of the last feature layer form a distinguished basis which is particularly useful for extracting semantic information. Another reason for DL success is that it avoids the need for the feature engineering process. Depending on the type of input (images, text, audio, etc.) Published in: IEEE Journal of Biomedical and Health Informatics ( Volume: 21 , Issue: 1 , Jan. 2017 ) It is worth mentioning the presence of these approaches in relevant EDM forums such as the annual International Conference in Educational Data Mining, with 7 papers published in the last edition (for a total of 16 in the last three years). Based on the eleven categories proposed by [4], they suggested a hierarchy of thirteen categories grouped into five main tasks: Student Modeling, Decision Support Systems, Adaptive Systems, Evaluation, and Scientific Inquiry. View Mohamad Ivan Fanany’s profile on Twitter, View Mohamad Ivan Fanany’s profile on LinkedIn, Follow Deep Learning for Big Data on WordPress.com, A Review on a Deep Learning that Reveals the Importance of Big Data, Review on A Deep Learning that Predict How We Pose from Motion, Review on A Paper that Combines Gabor Filter and Convolutional Neural Networks for Face Detection, Review on Deep Learning for Signal Processing, Review on A Deep Learning for Sleep Analysis, Review on The First Paper on Rectified Linear Units (The Building Block for Current State-of-the-art Deep Convolutional NN), Review on Famous Google's Deep Learning Paper. Some works described in this article use word embeddings to reduce the dimensionality of the input space. When the output layer unit is trained with the cross-entropy loss (using the softmax activation function), it represents a conditional distribution of the label given the input (and the training set presented so far). This study discussed trends and shifts in research conducted by this community, comparing its current state with the early years of EDM. Yeung, “Temporal models for predicting student dropout in massive open online courses,” in, M. Teruel and L. A. Alemany, “Co-embeddings for student modeling in virtual learning environments,” in, W. Wang, H. Yu, and C. Miao, “Deep model for dropout prediction in MOOCs,” in. Reference [38] proposed a hybrid recommendation system (called LeCoRe) that recommended learning opportunities to students based on their (implicit or explicit) preferences, allowing connecting them by similar interests on the platform. 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. This classification revealed that only 4 of the 13 tasks defined in that taxonomy have been faced using DL approaches: predicting students performance, detecting undesirable student behaviors, generating recommendations, and automatic evaluation. At that time, I concluded that this daily activity of paper-reading is crucial to keep my mind active and abreast of the latest advancement in the field of deep learning. Predicting student performance: the objective is to estimate a value or variable describing the students’ performance or the achievement of learning outcomes. The memory cell retains its value for a period of time as a function of its inputs and contains three gates that control information flow into and out of the cell: the input gate defines when new information can flow into the memory; the forget gate controls when the information stored is forgotten, allowing the cell to store new data; the output gate decides when the information stored in the cell is used in the output. Firstly, three basic models of deep learning are outlined, including multilayer perceptrons, convolutional neural networks, and … If training and validation errors are high, the system is probably underfitting (it can neither model the training data nor generalize to new data), and the number of epochs can be increased. Now on the eve of the new year of 2020, I can proudly say that I executed my 2019 new year resolution of “reading at least one new paper per week” with flying colors. Paper where method was first introduced: Method category (e.g. In [35] the authors set 2 hidden layers for each modality feature (e.g., eye gaze and head pose), adding up to 8 hidden layers. Another key factor in the development of DL has been the emergence of software frameworks like TensorFlow, Theano, Keras, and PyTorch, which have allowed researches to focus in the structure of the models rather than in low-level implementation details (see Section 5.5). The studies that disagree with Piech et al. The first part of this section shows taxonomy of the tasks addressed by EDM systems. Other popular datasets are KDD Cup 2010 and the datasets available at DataShop repository. In fact, it has been applied to all the EDM tasks covered by DL approaches: predicting students performance [21, 24, 53]; detecting undesirable student behaviors by predicting students dropout [28], predicting dialogue acts [33], modeling student behavior in learning platforms [29], and predicting engagement intensity [35]; generating recommendations [39]; and evaluation by doing stealth assessment [44], improving casual estimates from A/B tests [46], and automating essay scoring [41]. (v)Discuss future directions for research in DL applied to EDM based on the information gathered in this study. C. Romero, S. Ventura, M. Pechenizkiy, and R. Baker. In the second case, GPUs allow massive parallel computing to train bigger and deeper models. A recent study is described in [8]. The lower the value is, the slower the algorithm traverses the downward slope. These architectures consist of multiple layers with processing units (neurons) that apply linear and nonlinear transformations to the input data. The experiments put into question the notion that neural networks disentangle variation factors across coordinates. For example, what can such deep networks compute? Training the neural network means finding the right parameters setting (weights) for each processing unit in the network. For instance, [10] combined ASSISTments 2009-2010 with another two datasets: a sample of anonymized student usage interactions on Khan Academy (https://www.khanacademy.org/) (1.4 million exercises completed by 47,495 students across 69 different exercises) and a dataset of 2,000 virtual students performing the same sequence of 50 exercises drawn from 5 skills. The proposed method could estimate the gaze target location of each person in the image with accuracy substantially better than chance and higher than other traditional baseline methods. The main advantage of CNNs is their accuracy in pattern recognition tasks, such as image recognition, requiring considerably fewer parameters than FNNs. DL models include hyperparameters, which are variables set before optimizing the parameters (weights and bias) of the models. No distinction between individual high level units and random linear combinations of high level units, according to various methods of unit analysis. Create a free website or blog at WordPress.com. Authored by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton, and titled ImageNet Classification with Deep Convolutional Networks, this paper is regarded as one of the most influential papers in the field. As deep neural networks are both time-consuming to train and prone to overfitting, a team at Microsoft introduced a residual learning framework to improve the training of networks that are substantially deeper than those used previously. Regarding the number of layers, most of the implementation ranges from 1 to 6 layers: 1 hidden layer [10, 13, 14, 17–19, 24, 32, 49, 50, 53], 2 hidden layers [11, 15, 20, 21, 34, 44], 3 hidden layers [22], 4 hidden layers [23, 26, 27, 37, 40, 41], 5 hidden layers [25, 31], and 6 hidden layers [30, 38]. This resource was also used by [25]. Such regions can represent, for instance, the same objects from different viewpoints, which are relatively far (in pixel space), but which share nonetheless both the label and the statistical structure of the original inputs. One of the main disadvantages of RNNs is the issue of vanishing gradients, where the magnitude of the gradients (values used to update the neural network weights) gets exponentially smaller (vanish) as the network back propagates, resulting in a very slow learning of the weights in the lower layers of the RNN. Batch sizes used in the works reviewed include 10 [31, 38], 32 [19, 27, 33, 41], 48 [25], 100 [10, 11, 18], 500 [37], and 512 [23]. In this paper, we provide a review of deep learning-based object detection frameworks. This dataset includes 16,228 short answers selected from a total of 27,868 dialogues about physics. (ii)Detecting undesirable student behaviors: the focus here is on detecting undesirable student behavior, such as low motivation, erroneous actions, cheating, or dropping out. This is the paper that rekindled all the interest in Deep Learning. Using DL techniques, they obtained significantly better performance than traditional machine learning methods for all three definitions of dropout: participation in the final week, last week of engagement, and participation in the next week. In Section 2, we briefly introduce some fundamental concepts on online visual tracking and some closely related deep learning algorithms.In Section 3, we review the existing trackers based on deep learning from three aspects: network structure, network function, and network training.In Section 4, we report the experiment evaluations of … The learning rate employed in the works studied ranges from a minimum of 0.0001 [34, 36] to a maximum of 0.1 [31], with other values such as 0.00025 [23] and 0.01 [19, 29, 35, 41]. Dean, “Distributed representations of words and phrases and their compositionality,” in, I. Goodfellow, J. Pouget-Abadie, M. Mirza et al., “Generative adversarial nets,” in. The essays length is between 150 and 550 words. Finally, for the specific analysis of sociomoral reasoning maturity, [37] developed a corpus of 691 texts in French manually coded by experts, stating the level of maturity in a range from 5 (highest) to 1 (lowest). For example, VGG16 [59], a popular neural network architecture applied to image classification, has 138 million parameters. If we use one neural to generate a set of adversarial examples, we find that these examples are still statistically hard for another neural network even when it was trained with different hyperparemeters or, most surprisingly, when it was trained on a different set of examples. Before deep learning came along, most of the traditional CV algorithm variants for action recognition can be broken down into the following 3 broad steps: Local high-dimensional visual features that describe a region of the video are extracted either densely [ 3 … Hetal Gaudani 1M.E.C.E., 2HOD, 2Associate Professor 1,2Department of Computer Engineering, IIET, Dharmaj 3Department of Computer Engineering, GCET, Vallabh Vidhyanagar Weight Update. As mentioned before, DL does this mapping between inputs and objective outputs (i.e., what the network is expected to produce) using artificial neural networks composed of a large number of layers forming a hierarchy. Objective To systematically examine the design, reporting standards, risk of bias, and claims of studies comparing the performance of diagnostic deep learning algorithms for medical imaging with that of expert clinicians. This allowed expanding the number of relevant papers retrieved. They produce impressive performance without relying on any feature engineering or expensive external resources. We analyzed 16,625 papers to figure out where AI is headed next. The second relevant aspect of this work is the study of existing datasets used by DL models in educational contexts. All these studies used the LSTM implementation of DKT, although some of them introduced their own variants. DCR finds most relevant review/s from a repository of common reviews generated using historical peer reviews. Posted by Mohamad Ivan Fanany Printed version This writing summarizes and reviews the most intriguing paper on deep learning: Intriguing properties of neural networks. This dataset includes information about student interactions in the virtual environment, but not about the student’s body of knowledge. There are more sophisticated approaches such as using unsupervised stacked RBMs to choose these weights. The output layer unit of a neural network is a highly nonlinear function of its input. Most of the papers reviewed used SGD in the training phase [10, 18–20, 22, 27, 31–33, 36, 40, 41, 49, 50]. the sentiment analysis and deep learning techniques have been merged because deep learning models are effective due to their automatic learning capability. Instead of completely feedforward connections, RNNs may have connections that feed back previous or the same layer. However, the most important achievements of DL have taken place in the last ten years. Deep learning—In this review, deep learning is defined as neural networks with at least two hidden layers; Time—Given the fast progress of research in this topic, only studies published within the past five years were included in this review. Review of paper by Sinong Wang, Belinda Z. Li, Madian Khabsa et al (Facebook AI Research), 2020Originally published in Deep Learning Reviews on June 28, 2020.. The last column of this table indicates whether, in the experiments carried out in the paper, the DL approach outperformed baseline methods (“>’’), underperformed (“<’’), or obtained similar results, with higher performance in some of the evaluations and lower performance in others (“=’’). The first one was carried out by Bakhshinategh et al. These algorithms are used for various purposes like data mining, image processing, predictive analytics, etc. In this paper, various machine learning algorithms have been discussed. The first property is concerned with the semantic meaning of individual units. In this case, the dataset contained information about the degree of success of 524 students answering several tests about probability. This mapping can be done using neural network approaches [98]. Among those analyzed, learning rate, batch size, and the stopping criteria (number of epochs) are considered to be critical to model performance. As a result, a large set of papers was retrieved and a manual review process was applied to filter out duplicates and papers on unrelated topics. Reference [25] proposed a model to categorize students into high, medium and low, to determine their learning capabilities and help them to improve their study techniques. Each node in the network is a neuron, which is the basic processing unit of a neural network. These studies performed video analysis to identify the loss of interest in the contents of the course, extracting features such as the student’s gaze. This information is summarized in the last two columns of Table 2. An interesting aspect of this work is the development of a novel taxonomy of tasks in EDM. In order to control the quality of the output of the neural network, it is necessary to measure how close is the obtained output from the expected output. Our review begins with a brief introduction on the history of deep learning and its representative tool, namely Convolutional Neural Network (CNN). In this respect, more training data means almost always better DL models. In principle, this could be considered a good starting point to develop a system in any of the tasks covered. The latest advances in deep learning technologies provide new effective paradigms to obtain end-to-end learning models from complex data. Peña-Ayala proposed in 2014 a thorough survey by applying data mining techniques to more than 240 papers in EDM [7]. Reference [36] used a validation set for early stopping, whereas [33] defined a strategy consisting in stopping the training if there is no improvement in the last 15 epochs (with a maximum of 100 epochs). The type of hidden layers defines the different neural network architectures, such as CNN, RNN, or LSTM (see Section 5.3). The adversarial examples represent low-probability (high-dimensional) “pockets” in the manifold, which are hard to efficiently find by simply randomly sampling the input around a given example. The specification of what each layer is doing to the input received is stored in the weights of the layer. The most repeated values are 0.2 [11, 27, 34] and 0.5 [19, 23, 41], followed by 0.3 [29, 36]. The main representatives of this type of networks are perceptron and Multilayer Perceptron (MLP). (iii)Profiling and grouping students: the purpose is to profile students based on different variables, such as knowledge background, or to use this information to group students for various purposes. For this purpose, the Kaggle platform has been used to obtain datasets for automated essay scoring. Challenge data set from KDD Cup 2010 Educational Data Mining Challenge,” 2010, K. Koedinger, R. Baker, K. Cunningham, A. Skogsholm, B. Leber, and J. Stamper, “A data repository for the EDM community: The PSLC DataShop,” in, L. Wang, A. Sy, L. Liu, and C. Piech, “Learning to represent student knowledge on programming exercises using deep learning,” in, H. Singh, S. K. Saini, R. Chaudhry, and P. Dogga, “Modeling hint-taking behavior and knowledge state of students with multi-task learning,” in, W. G. Hatcher and W. Yu, “A survey of deep learning: platforms, applications and emerging research trends,”, J. Schmidhuber, “Deep learning in neural networks: an overview,”, P. Domingos, “A few useful things to know about machine learning,”, G. Zhong, L. Wang, X. Ling, and J. Dong, “An overview on data representation learning: From traditional feature learning to recent deep learning,”. The authors declare that there are no conflicts of interest regarding the publication of this paper. 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. Di Caro et al., “Max-pooling convolutional neural networks for vision-based hand gesture recognition,” in, A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in, S. Ji, W. Xu, M. Yang, and K. Yu, “3D Convolutional neural networks for human action recognition,”, D. Silver, A. Huang, C. J. Maddison et al., “Mastering the game of Go with deep neural networks and tree search,”, R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, and P. Kuksa, “Natural language processing (almost) from scratch,”, J. J. Hopfield, “Neural networks and physical systems with emergent collective computational abilities,”, A. Graves, A.-R. Mohamed, and G. Hinton, “Speech recognition with deep recurrent neural networks,” in, N. Kalchbrenner, E. Grefenstette, and P. Blunsom, “A convolutional neural network for modelling sentences,” in, K. Cho, B. These figures are not surprising given the successful results of DL techniques in many different domains. Another aspect to take into account is the size of the network. This repository is home to the Deep Review, a review article on deep learning in precision medicine.The Deep Review is collaboratively written on GitHub using a tool called Manubot (see below).The project operates on an open contribution model, welcoming contributions from anyone (see CONTRIBUTING.md or an existing example for more info). Also in the task of knowledge tracing, but away from the controversy initiated by Piech et al., the work in [20] proposed also a DL classifier to predict whether students will fail or pass an assignment. "deep learning" AND "educational data mining". The key difference is the feedback mechanisms within the network, which can manifest in a hidden layer, in the output layer or in a combination of them. 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. TensorFlow (https://www.tensorflow.org/) is the second most popular framework in this list. C. Yeung and D. Yeung, “Addressing two problems in deep knowledge tracing via prediction-consistent regularization,” 2018, P. P. Bendangnuksung, “Students’ performance prediction using deep neural network,”, S. Tang, J. C. Peterson, and Z. This approach was later employed to personalize retention tests. This proposal was not compared with traditional machine learning methods. Base on experiments using convolutional neural networks trained on MNIST and AlexNet. In this paper, we aim to provide a comprehensive review on deep learning methods applied to answer selection. In this regard, the most commonly used configuration values were: 0.0001 and 0.01 learning rate; 32 and 100 batch size; 0.9 momentum; SGD weighting update; 50 epochs stopping criteria; 1 or 2 hidden layers depth; 100 or 200 hidden units per layer width; random weight initialization; and 0.2 dropout. Early stopping is a form or regularization used to avoid overfitting. It includes additional information such as clickstream data about answers to quiz questions, play/pause/rewind events on lecture videos, and reading and writing to the discussion form. Deep Learning for Video Captioning: A Review Shaoxiang Chen1, Ting Yao3 and Yu-Gang Jiang1;2 1Shanghai Key Lab of Intelligent Info.Processing, School of Computer Science, Fudan University, China 2Jilian Technology Group (Video++), Shanghai, China 3JD AI Research, China fsxchen13, ygjg@fudan.edu.cn, yaoting5@jd.com as a better choice rather than using randomly initialized weights [67]. This can never occur with smooth classifiers by their definition. In this study, we provide a comprehensive review of deep learning-based recommendation approaches to enlighten and … This research was published in the paper titled Deep Residual Learning for Image Recognition in 2015. 5. Profiling and grouping students: the purpose is to profile students based on different variables, such as knowledge background, or to use this information to group students for various purposes. Reference [32] presented a specific dataset for student dropout analysis created from a project management MOOC course hosted by Canvas. Reference [30] combined different DL architectures in a bottom-up manner, selecting three attributes from the dataset as an input. Early stopping rules provide a guide to identify how many iterations can be run before overfitting. This paper analyzes and summarizes the latest progress and future research directions of deep learning. Related to multimodal interactions, [33] developed a dataset of students interactions within a game-based virtual learning environment called Crystal Island. Keras provides a Python interface to facilitate the rapid prototyping of different deep neural networks, such as CNNs and RNNs, which can be executed on top of other more complex frameworks such as TensorFlow and Theano (see below). Now on the eve of the new year of 2020, I can proudly say that I executed my 2019 new year resolution of “reading at least one new paper per week” with flying colors. DL is a subfield of machine learning that uses neural network architectures to model high-level abstractions in data. The paper provides a systematic review on the application of deep learning in SHM. There is a lack of end-to-end learning solutions and appropriate benchmarking mechanisms. The second part of the section describes the main datasets used in the field, also grouped by the task addressed. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound and major anatomical structures of interest (ventricles, … We found a way to traverse the manifold represented by the network in an efficient way (by optimization) and finding adversarial examples in the input space. This is an interesting dataset since it combines content-based resources that show student knowledge with data about student behavior in an online educational platform. This helps to avoid missing local minima, but on the downside it takes a long time to converge and arrive at the best accuracy of the model. They extracted information from a ITS called Pyrenees. Why can they generalize? Our study of 25 years of artificial-intelligence research suggests the era of deep learning may come to an end. The paper proposes a scheme to make input deformation process adaptive in a way that exploits the model and its deficiencies in modeling the local space around the training data. Most of them have been published in conferences (80%). 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. The work by [21] leveraged a DL model to explore two different contexts within the educational domain: writing samples from students and clickstream activity within a MOOC. Deep learning—In this review, deep learning is defined as neural networks with at least two hidden layers; Time—Given the fast progress of research in this topic, only studies published within the past five years were included in this review. At a certain point, improving the model fit to the training data increases generalization errors. EDM is concerned with developing, researching, and applying machine learning, data mining, and statistical methods to detect patterns in large collections of educational data that would otherwise be impossible to analyze [1]. Enjoy the videos and music you love, upload original content, and share it all with friends, family, and the world on YouTube. The results showed that the proposed model could achieve comparable performance to approaches relying on feature engineering performed by experts. Unfortunately, it seems that the data is no longer available. This reveals that there are many open opportunities for the use of DL in unexplored EDM tasks, moreover taking into account the promising results obtained by these models in the works reviewed (67% of them reported that DL outperformed the “traditional” machine learning baselines in all their experiments).

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