Conventional machine-learning techniques were limited in their 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 or an existing example for mor… The execution of statistical and clustering processes identified a set of educational functionalities, a pattern of EDM approaches, and two patterns of value-instances to depict EDM approaches based on descriptive and predictive models. 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. Transfer learning [95] was used in [36] to initialize CNNs with weights pretrained on ImageNet. 1 Introduction Answer selection is an active research field and has drawn a lot of attention from the natural language processing community. The result is that the neural network is less sensitive to specific weights of neurons achieving better generalization. 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. 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. The largest dataset for the analysis of student dropout was presented in [31]. In this case, the dataset contained information about the degree of success of 524 students answering several tests about probability. (iv)Introduce key DL concepts and technologies, describing the techniques and configurations most widely used in EDM and its specific tasks. This dataset is often used jointly with others. This is exactly the aim of ASSISTment ( [47, 48]. FNNs are primarily used for supervised learning tasks where the input data is neither sequential nor time-dependent, offering good results when the number of layers, neurons and training data is large enough. This suggests that there is room for applying more complex and deep architectures in the field of EDM. 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. Given the increasing adoption of DL techniques in EDM, this work can provide a valuable reference and a starting point for researches in both DL and EDM fields that want to leverage the potential of these techniques in the educational domain. Another reason for DL success is that it avoids the need for the feature engineering process. Many research fields have benefited from applying these technologies, and EDM is not an exception. Some works described in this article use word embeddings to reduce the dimensionality of the input space. (v)Discuss future directions for research in DL applied to EDM based on the information gathered in this study. For this purpose, the Kaggle platform has been used to obtain datasets for automated essay scoring. Previous works considers a state-of-the-art deep neural network that generalizes well on an object recognition task can be expected to be robust to small perturbations of its input, because small perturbation cannot change the object category of an image. In 2009, a new EDM survey was presented by Baker and Yacef [6]. The problem is that DL networks may potentially have millions of these parameters and finding the correct values for all of them can be a really difficult task. 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 this paper, we provide a review on deep learning based object detection frameworks. The works by [23, 30, 50] used this framework. This mapping can be done using neural network approaches [98]. Some of the works studied reported dropout values in their network configurations. 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. The results demonstrated significant improvement compared to traditional state-of-the-art methods. The main goals of this study are to identify the EDM tasks that have benefited from Deep Learning and those that are pending to be explored, to describe the main datasets used, to provide an overview of the key concepts, main architectures, and configurations of Deep Learning and its applications to EDM, and to discuss current state-of-the-art and future directions on this area of research. 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. Sign up here as a reviewer to help fast-track new submissions. 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]. The error calculated by this function is fed back through the network, usually by means of backpropagation. 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. This corpus comprises 40 MOOCs from HarvardX with information about number of registered participants and number of participants who certified. Motivations: Deep neural networks are highly expressive models that have recently achieved state of the art performance on … CNNs are similar to FNNs in different aspects: they are composed of neurons where bias and weights have to be learned, each neuron has some inputs, performs a dot product, and applies an activation function, and there is a loss function in the last (fully connected) layer that measures the difference between the predicted and the expected value. References [16, 50] combined also these datasets and used in addition data collected from the Knewton adaptive learning platform ( In [23], the authors presented a DL classifier for predicting students' performance, which took advantage of a relatively large real world students' dataset of unlabeled data. This section presents an overview of the main datasets used for EDM in the reviewed papers, as well as other datasets developed for specific studies. The rest are specific datasets used in individual studies, which extract data (mainly exercises with real answers) from educational platforms or ITS such as Khan Academy, Woot Math, Udacity, Knewton, Funtoot, and Cordillera. The final set contained 41 papers. The second task, automatic essay scoring, is a hard challenge that requires a deep linguistic analysis to achieve automatic evaluations of texts. This library was used in the work by [35]. In this subtask the goal is to predict student’s future performance based on their past activity. Regarding educational platforms, [26, 27] compiled several datasets with information about 30,000 students in Udacity ( DL algorithms learn multiple levels of data representations, where higher-level features are derived from lower level features to form a hierarchy. We review recent work in which methods of … Finally, the work by [37] proposed a DL model to evaluate sociomoral reasoning maturity, a key social ability necessary for adaptive social functioning. As already mentioned in Section 4.1.1, there is a controversy between a set of studies, falling in the task of predicting students performance, which have focused on knowledge tracing, i.e., modeling the knowledge of students as they interact with coursework. 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 … Conventional machine-learning techniques were … RNNs can be trained with standard backpropagation or by using a variant called backpropagation through time (BPTT) [78]. There is an open-source machine learning library for Python based on Torch, called PyTorch (, which has gained increasing attention from the DL community since its release in 2016. Batch Size. Using a batch size lower than the number of all samples has some benefits, such as requiring less memory (the network is trained using fewer samples in each propagation) and training faster (weights are updated after each propagation). In another work, [39] focused on the less investigated problem of curriculum planning for students, providing a novel approach to this domain based on two components: a DL approach to sequential recommendations and a recommender to provide a personalized pathway to completion using sequence, constraint, and contextual parameters. Autoencoders (and its variants stacked, sparse and denoising) are typically used to learn compact representations of data [66]. In their research, the authors used a subset containing only undergraduate Engineering and IT students information. This is the paper that rekindled all the interest in Deep Learning. Based on the taxonomy of EDM applications defined by [8], only 4 of the 13 tasks proposed in that study have been addressed by DL techniques. With respect to the performance of DL techniques in these works, leaving aside the papers that do not offer a comparison between DL and traditional machine learning techniques, 67% of the works reported that DL outperformed the existing baselines, 27% showed inconclusive results (DL performed better only in some of the experiments), and only 6% reported a lower performance of DL techniques. The bibliography cited in the papers that initially passed the filter was also reviewed. Table 1 summarizes the number of papers published in each publication venue. 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. 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. 2019, Article ID 1306039, 22 pages, 2019., 1Technical University of the North, Ecuador. They consist of a single layer of output nodes, where inputs are sent directly to the output via a series of weights. Deep Learning approaches in the EDM field: architectures employed, baseline methods, and evaluation measures. The first one was described in [38] and presents a dataset of learner’s profile information and the courses they have enrolled or completed. In the task of predicting student performance, a large sample of the papers analyzed were devoted to compare the performance of BKT (probabilistic) and DKT (deep learning) models, resulting in an interesting discussion between traditional and deep learning approaches (see Section 5.3.4). DL obtained the best performance in their experiments. 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 or an existing example for more info). This architecture is similar to MLP, but in this case the output layer has the same number of neurons as the input layer. 61.66% of the corpus is labeled as “correct” while the rest is labeled as “incorrect”. The paper mainly focuses on key applications of deep learning in the fields of translational bioinformatics, medical imaging, pervasive sensing, medical informatics, and public health. Each neuron is connected to many others and the links between them can increment or inhibit the activation state of the adjacent neurons. 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.. Each circular node represents a neuron. Based on the analyzed work, we suggest that deep learning approaches could be The most common initialization procedure in the papers reviewed is to randomly select the initial weights: Gaussian distribution with zero mean and small variance [19], uniform weights in the range [20, 28, 44], and uniform weights in the range [13]. Creating alerts for stakeholders: the objective is to predict student characteristics and detect unwanted behavior, serving as an online tool for informing stakeholders or creating alerts in real time. The analysis presented was based on four dimensions: computer supported learning analytics, computer supported predictive analytics, computer supported behavioral analytics, and computer supported visualization. Nevertheless, a general advice with deep neural networks is to take many small steps (smaller batch sizes and learning rates) instead of fewer larger ones, although this is a design trade-off that requires experimentation. Paper where method was first introduced: Method category (e.g. Another ITS used in these works is Funtoot ( The results indicated that coembeddings were able to capture the latent causes involved in dropout, outperforming other disjoint and not embedded representations. Both game actions and parallel sensor data were captured to collect cognitive and affective features. Such is the case of [11]. Machine learning, especially its subfield of Deep Learning, had many amazing advances in the recent years, and important research papers may lead to breakthroughs in technology that get used by billio ns of people. The formal description of how to generate adversarial examples is given. Training the neural network means finding the right parameters setting (weights) for each processing unit in the network. 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). Increasingly, these applications make use of a class of techniques called deep learning. This feedback allows RNNs to keep a memory of past inputs. The paper provides a systematic review on the application of deep learning in SHM. The challenge proposed in this competition was to predict student dropout on XuetangX, one of the largest MOOC platforms in China. In this article, we review the recent literature on applying deep learning technolo-gies to advance the health care domain. 5. Reference [17] presented a large dataset combining different resources: the ASSISTments 2009-2010 dataset, a synthetic dataset developed by [10], a dataset of 578,726 trials from 182 middle-school students practicing Spanish exercises (translations and simple skills such as verb conjugation), and a dataset from a college-level engineering statics course comprising 189,297 trials of 1,223 exercises from 333 students [52] ( There is a set of general purpose datasets that have been developed to address this task. MLP consists of multiple layers of neurons, where each neuron in one layer has directed connections to the neurons of the following layer. (ii)Detecting undesirable student behaviors: the focus here is on detecting undesirable student behavior, such as low motivation, erroneous actions, cheating, or dropping out. LeCoRe combined both content-based and collaborative filtering techniques in its phases. Weight Update. Initially, the weights of each neuron can be assigned randomly, or follow some initialization strategy, including unsupervised pretraining [60]. Adding more layers (depth) and neurons (width) can lead to more powerful models, but these architectures are also easier to overfit. This dataset includes information about student interactions in the virtual environment, but not about the student’s body of knowledge. 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. Given a question and a set of candidate answers, the task is to identify … Premal J Patel, 3Prof. In [34] the assumption was that if educational videos are not engaging, then students tend to lose interest in the course content. There are two works addressing the recommendation of learning items to assist students. 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). The use of machine learning (ML) has been increasing rapidly in the medical imaging field, including computer-aided diagnosis (CAD), radiomics, and medical image analysis.