Other configurations include 5 [31], 15 [44], 20 [28], 40 [37], and 300 [20]. Some works described in this article use word embeddings to reduce the dimensionality of the input space. 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 … Reference [32] presented a specific dataset for student dropout analysis created from a project management MOOC course hosted by Canvas. Note that not all the papers reviewed provide implementation details. A Systematic Review of Deep Learning Approaches to Educational Data Mining, Technical University of the North, Ecuador. Reference [35] presented also a multimedia dataset for engagement prediction. 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. Among those analyzed, learning rate, batch size, and the stopping criteria (number of epochs) are considered to be critical to model performance. There is a lack of end-to-end learning solutions and appropriate benchmarking mechanisms. About The Paper. We analyzed 16,625 papers to figure out where AI is headed next. Finally, [31] used Net2Net, a technique to accelerate transfer learning from a previous network to a new one [96]. 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. They pretrained hidden layers of features using an unsupervised sparse autoencoder from unlabeled data, and then used supervised training to fine-tune the parameters of the network. Instead of completely feedforward connections, RNNs may have connections that feed back previous or the same layer. Motivations: Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. Early stopping rules provide a guide to identify how many iterations can be run before overfitting. This mapping can be done using neural network approaches [98]. The type of hidden layers defines the different neural network architectures, such as CNN, RNN, or LSTM (see Section 5.3). Finally, compared to this architecture, LSTMs reduce the amount of training data required to build the models. These data was extracted from the Cognitive Algebra Tutor system during 2005 and 2006 [51]. The research in this field is developing very quickly and to help our readers monitor the progress we … The output layer unit of a neural network is a highly nonlinear function of its input. 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). There are more sophisticated optimization methods such as limited memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) and conjugate gradient (CG) that can speed up the process of training DL algorithms [91]. RNNs have been successfully applied to a variety of problems such as speech recognition [75], language modeling [76], and machine translation [77]. Conventional machine-learning techniques were limited in their Adaptive systems: this task is related to the use of intelligent systems in computer based learning, where the system has to adapt to the user’s behavior. Adding more layers (depth) and neurons (width) can lead to more powerful models, but these architectures are also easier to overfit. This study discussed trends and shifts in research conducted by this community, comparing its current state with the early years of EDM. Most approaches are application specific with no clear way to select, design or implement an architecture. Posted by Mohamad Ivan Fanany Printed version This writing summarizes and reviews the most intriguing paper on deep learning: Intriguing properties of neural networks. Increasingly, these applications make use of a class of techniques called deep learning. While DKT usually obtained better performance, BKT offered better interpretation of its predictions. Since DL is a very active research topic, it is expected that advances in DL will provide in a future theoretical understanding and interpretability of the models generated, and these findings will benefit all the fields where DL is applied, including EDM. Neural networks are computational models based on large sets of simple artificial neurons that try to mimic the behavior observed in the axons of the neurons in human brains. When the gradient keeps pointing in the same direction, this increases the size of the steps taken towards the minimum. Focusing in EDM, the work by [23] used a sparse autoencoder in the task of predicting students performance. Finally, [44] presented a dataset of 244 middle-school students’ problem-solving behaviors collected from interactions within a game-based learning environment. Finally, other studies used their own platforms to gather the data. B. Kim, E. Vizitei, and V. Ganapathi, “Gritnet 2: Real-time student performance prediction with domain adaptation,” 2018. The following subsections present each task and the works related in more detail. This paper surveys the research carried out in Deep Learning techniques applied to EDM, from its origins to the present day. There is an open-source machine learning library for Python based on Torch, called PyTorch (https://pytorch.org/), which has gained increasing attention from the DL community since its release in 2016. This review paper provides a brief overview of some of the most significant deep learning schemes used in computer vision problems, that is, Convolutional Neural Networks, Deep Boltzmann Machines and D… Conditional Neural Fields (CNF) are an extension of Conditional Random Fields (CRFs) The loss function used here is derived by Conditional Random Field, trying to account for the SS interdependency among adjacent residues. 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 amount by which the weights are changed is determined by a parameter called learning rate (see Section 5.4). Unfortunately, it seems that the data is no longer available. Indeed, according to [56], 54% of the works reviewed could be considered “shallow” neural networks, since they only include 1 or 2 hidden layers in their architectures. In their research, the authors used a subset containing only undergraduate Engineering and IT students information. Liou, W.-C. Cheng, J.-W. Liou, and D.-R. Liou, “Autoencoder for words,”, S. Chandar, S. Lauly, H. Larochelle et al., “An autoencoder approach to learning bilingual word representations,” in, D. Erhan, Y. Bengio, A. Courville, P.-A. 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. Depth and Width. Reference [21] combined the Kaggle ASAP dataset with clickstream data from a BerkeleyX MOOC from Spring 2013. In theory, larger batch sizes imply more stable gradients, facilitating higher learning rates. The output layer provides the predictions of the model. Machine-learning systems are used to identify objects in images, transcribe speech into text, match news items, posts or products with users’ interests, and select relevant results of search. In addition to general graph data structures and processing methods, it contains a variety of recently published methods from the domains of relational learning … In recent years, deep learning techniques revolutionized the way remote sensing data are processed. Reference [34] also developed a multimedia corpus for the analysis of liveliness of educational videos. Given the empirical nature of the development process of DL models, there is no one-size-fits-all solution to set the best configuration for a specific architecture, and the hyperparameters chosen will depend on the input data available and the task at hand. As mentioned in Section 4.1.4, the task of evaluation comprises two main subtasks: automated essay scoring and automatic short answer grading. The essays length is between 150 and 550 words. 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. Choropleth map showing the density of researchers per country in the papers reviewed based on their affiliation. 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. For instance, these platforms record when the students access a learning object, how many times they accessed it, whether the answer provided to an exercise is correct or not, or the amount of time spent reading a text or watching a video. All this information can be analyzed to address different educational issues, such as generating recommendations, developing adaptative systems, and providing automatic grading for the students’ assignments. The work by [49] also combined ASSISTments 2009-2010, in this case with the OLI Engineering Statics dataset (https://pslcdatashop.web.cmu.edu/Project?id=48), which included college-level engineering statics. The compared images appear to be semantically meaningful for both the single unit and the combination of units. The International Conference on Educational Data Mining accumulates the maximum number of publications (considering the last three editions), with a total of 16. Authors are weighted by the number of contributors to the paper. Review articles are excluded from this waiver policy. This can never occur with smooth classifiers by their definition. C. Romero, S. Ventura, M. Pechenizkiy, and R. Baker. Stopping Criteria. It is the space, rather than the individual units, that contains of the semantic information in the high layers of neural networks. The recent striking success of deep neural networks in machine learning raises profound questions about the theoretical principles underlying their success. 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/). For each possible score in the rubric, student responses graded with the same score were collected and used as the grading criteria. 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 mor… This feedback allows RNNs to keep a memory of past inputs. Paper where method was first introduced: Method category (e.g. 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]. K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” 2014, Y. Bengio, “Practical recommendations for gradient-based training of deep architectures,” in, F. Rosenblatt, “The perceptron: a probabilistic model for information storage and organization in the brain,”, C.-Y. Different DL architectures have been developed and successfully applied to different supervised and unsupervised tasks in the broad fields of natural language processing and computer vision [55]. Our review begins with a brief introduction on the history of deep learning and its representative tool, namely, the convolutional neural network. This study has reviewed the emergence of DL applications to EDM, a trend that started in 2015 with 3 papers published, increasing its presence every year so far with 17 papers published in 2018. As the models change, previous choices may no longer be the best ones. DCR finds most relevant review/s from a repository of common reviews generated using historical peer reviews. Previous works analyzed the semantic meaning of various units by finding the set of inputs that maximally activate a given unit. Other works used Adam [25, 38], an efficient gradient descent algorithm [92]. AES systems are used to evaluate and score written student essays based on a given prompt. Major companies such as Google, Facebook, Microsoft, Amazon, and Apple are heavily investing in the development of software and hardware innovations in this field, trying to leverage DL potential in the production of smart products. The difference with conventional LSTMs is that these networks only preserve information from the past, whereas BLSTMs run inputs in two ways: one from past to future and other from future to past, preserving information from the future in the backward run [89]. Deep Learning approaches in the EDM field: architectures employed, baseline methods, and evaluation measures. Published in: IEEE Journal of Biomedical and Health Informatics ( Volume: 21 , Issue: 1 , Jan. 2017 ) 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 dataset includes information about student interactions in the virtual environment, but not about the student’s body of knowledge. The Deep Review. How can we train them? Based on the taxonomy of EDM applications defined by [8], the papers reviewed on the present study were categorized according to the problem addressed. The work by [36] defined 16 (since it employs the VGG16 architecture). Unit-level inspection methods had relatively little utility beyond confirming certain intuitions regarding the complexity of the representations learned by a deep neural network. This data was a multilevel representation of student related information: demographic data (e.g., gender, age, health status, and family status), past studies, school assessment data (e.g., school type and school ranking), study data (e.g., middle-term exam, final-term exam, and average), and personal data (e.g., personality, attention and psychology related data). This is an example of unsupervised learning, since no labeled data is required. This paper analyzes and summarizes the latest progress and future research directions of deep learning. Figure 1 summarizes the number of publications per year. One of the challenges that has gained more attention in this area is knowledge tracing. The paper visually compared images that maximize the activations in the natural basis and images that maximize the activation in random directions. DL algorithms learn multiple levels of data representations, where higher-level features are derived from lower level features to form a hierarchy. Pyrenees was also used in [15] (68,740 data points from 475 students) together with other dataset collected from a natural language physics ITS, named Cordillera, that teaches students introductory college physics (44,323 data points from 169 students). Regarding educational platforms, [26, 27] compiled several datasets with information about 30,000 students in Udacity (https://www.udacity.com). To these end, a DL-based dialogue act classifier that utilizes these three data sources was implemented. This architecture is similar to MLP, but in this case the output layer has the same number of neurons as the input layer. This recurrent unit has fewer parameters than LSTMs, since it has two gates instead of three, lacking an output gate. The work by [35] was focused on the movements of gaze and pose to determine the engagement intensity while watching online educational courses videos. Reference [42] studied answer-based, questions, and student models features, both individually and combined, integrating them in different machine learning models. Our review begins with a brief introduction on the history of deep learning and its representative tool, namely Convolutional Neural Network (CNN). No distinction between individual high level units and random linear combinations of high level units, according to various methods of unit analysis. Reference [43] presented a corpus of short answer question responses from students, but in this case the topic of the course was human biology. 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 There are different ways to determine the number of epochs employed to train the algorithms. Although not all the studies analyzed in this article provide details about the hyperparameters used, references are provided when available. In order to perform a systematic review, the following scientific repositories were accessed: ACM Digital Library (https://dl.acm.org/), Google Scholar (https://scholar.google.es/), and IEEE Xplore (https://ieeexplore.ieee.org/). This layer then takes this simple information, combines it with something more complex, and sends it to a third layer. In conjunction with CNNs, LSTMs have been used to produce image [84] and video [85] captioning: the CNN implements the image/video processing whereas the LSTM converts CNN output into natural language. Summary of EDM tasks, approaches, datasets, and types of datasets. Results showed an improvement with respect to other approaches requiring feature engineering. The first layer is the input layer, which is used to provide input data or features to the network. "deep learning" AND "educational data mining". The proposal significantly outperformed the baseline method proposed. Training the neural network means finding the right parameters setting (weights) for each processing unit in the network. https://sites.google.com/site/assistmentsdata/home/, https://sites.google.com/view/edm-longitudinal-workshop/, http://pslcdatashop.web.cmu.edu/KDDCup/downloads.jsp, International Conference on Educational Data Mining (2016, 2017, 2018), Third ACM Conference on Learning @ Scale (2016, 2017), IEEE International Conference on Data Mining Workshop (ICDMW 2015), International Symposium on Educational Technology (ISET), Seventh International Learning Analytics and Knowledge Conference, Annual Conference on Neural Information Processing Systems (NIPS), Conference on Empirical Methods in Natural Language Processing (2016), 26th Conference on User Modeling, Adaptation and Personalization, 2nd International Conference on Crowd Science and Engineering, Neural Information Processing Systems, Workshop on Machine Learning for Education, 2nd International Conference on Innovation in Artificial Intelligence, 20th ACM International Conference on Multimodal Interaction, International Journal of Applied Engineering Research, Journal of Engineering and Applied Sciences, Journal of Educational Computing Research, Predicting student performance, achievement of learning outcomes or characteristics, Kaggle Students’ Academic Performance dataset, ASSISTment 2009-2010, KDD Cup 2010 and ITS Knewton, ASSISTment 2009-2010 dataset, KDD Cup 2010 dataset and ITS Knewton, Assistment 2009-2010 dataset, virtual student dataset, and data from Spanish and Engineering courses, ASSISTment 2009-2010 dataset, KDD Cup 2010 dataset and ITS Woot Math, Virtual student dataset and Assistments 2009-2010 dataset, ASSISTment 2009, ASSISTment 2015, ASSISTment Challenge, Statics2011, Simulated-5, ASSISTment 2009-2010 dataset and KDD Cup 2015, Game-based virtual learning environment Crystal Island, Videos collected in unconstrained environments, problem-solving dataset from game-based learning environment, ASSISTment 2009-2010 and Kaggle Automated Essay Scoring, Short-answer question dataset from biology course, Accuracy, AUC, Precission, Recall, F-measure. 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. It updates the network so as to make it better fit the training data with each iteration, improving also the model performance on the validation dataset. The prediction of dropping out in MOOC platforms is the subtask that has gained more attention in detecting undesirable student behaviors. Finally, Figure 2 shows a choropleth map of the world showing the density of researchers per country involved in the area of DL applied to EDM, based on their affiliation. References [12, 30] used a corpus of programming exercises (http://code.org/research) that contains 1,263,360 code submissions about multiple concepts such as loops, if-else statements and nested statements. B. Kim, E. Vizitei, and V. Ganapathi, “Gritnet: Student performance prediction with deep learning,” 2018, M. Fei and D.-Y. In the last years, different surveys have focus in different aspects of EDM systems. The works by [23, 30, 50] used this framework. Unlike LSTMs, a RNN may leave out important information from the beginning while trying to process a paragraph of text to do predictions. Both studies focused on generating personalized searches based on their preferences and curriculum planning. The analysis presented was based on four dimensions: computer supported learning analytics, computer supported predictive analytics, computer supported behavioral analytics, and computer supported visualization. MLP consists of multiple layers of neurons, where each neuron in one layer has directed connections to the neurons of the following layer. The number of hidden layers determines the depth of the network. In this paper, we aim to provide a comprehensive review on deep learning methods applied to answer selection. The rest of the paper is organized as follows. Depending on the type of input (images, text, audio, etc.) I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks. How does information propagate through them? This Review Paper highlights latest studies regarding the implementation of deep learning models such as deep neural networks, convolutional neural networks and Reference [13] also combined ASSISTments 2009-2010 dataset, in this case with KDD Cup 2010, and with a dataset collected by the Woot Math system (https://www.wootmath.com/). The Hybrid Imaging System Laboratory (HISLab) from the Smart Medical Information Research Center published a manuscript entitled “Review of deep learning for photoacoustic imaging” based on all the research using deep learning to solve various problems in photoacoustic imaging in recent years. Transfer learning [95] was used in [36] to initialize CNNs with weights pretrained on ImageNet. There is a lack of end-to-end learning solutions and appropriate benchmarking mechanisms. Then this weight can be used for the deep network (with the same configuration in terms of hidden layers, number of neuros per layer, etc.) The high dimensionality of hyperspectral images and the availability of simulated spectral sample libraries make deep learning an appealing approach. Traditional computer vision systems rely on feature extraction: often a single feature is easily interpretable. The topic of Deep Learning (DL) has gained increasing attention in the industry and research areas in the last decade, revolutionizing the field of machine learning by obtaining state-of-the-art results in perception tasks such as image and speech recognition [2]. 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 resource was also used by [25]. In this respect, more training data means almost always better DL models. Table 2 summarizes these four tasks in EDM (first column), the references to the works in the field (second column), the datasets employed (third column), and the types of datasets (fourth column). Base on experiments using convolutional neural networks trained on MNIST and AlexNet. Deep Learning Methods and Developments.. Convolutional Neural Networks. The latest advances in deep learning technologies provide new effective paradigms to obtain end-to-end learning models from complex data. In this way, researchers can focus on the architecture of the model and overlook low-level details. The paper demonstrates the advantages of CuLE by effectively training agents with traditional deep reinforcement learning algorithms and measuring the utilization and throughput of … 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. The learning rate controls how much the weights of the network are adjusted with respect to the loss gradient. Basic structure of a neural network. On the negative side, they have disadvantages such as the high computation cost, the need for large amounts of training data, and the work required to properly initialize the network according to the problem addressed. This writing summarizes and reviews the famous Google’s deep learning paper: Building high-level features using large scale unsupervised learning. This controversy has also arisen in EDM, with the aforementioned arguments for and against DKT and BKT. They produce impressive performance without relying on any feature engineering or expensive external resources. Based on the some experiments in the paper, however, the smoothness assumption that underlies many kernel methods does not hold. Other values reported are 0.25 [50], 0.4 [49], 0.6 [13], and 0.7 [33]. The log data represented the learning activities of students who used the LMS, the e-portfolio system, and the e-book system. LeCoRe combined both content-based and collaborative filtering techniques in its phases. With respect to the number of units per hidden layer, the most common value in the papers reviewed is 200 [10, 11, 14, 15, 17–19, 49], followed by 100 [22, 40, 50], 64 [33, 35], 128 [21, 27], and 256 [26, 34]. Only three papers in EDM explicitly stated the use of momentum, all of them with a value of 0.9 [23, 35, 36]. This information is used to adjust the weights of each connection in the network in order to reduce the error. Such is the case of [11]. 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).