A method for the analysis of data from online educational research

The intention of this article is to provide an alternative method of data analysis for online learning and VLE related research that is essentially paper based. The article describes the use of a paper-based method for data analysis of online learning type research that involves the collection and collation of electronic (and possibly also paper based) data. This method partly builds on the work of Tyler (2001) and has been used on research projects that investigated online learning as a method for widening participation (Hramiak, 2001a, 2002a) and also on a project that involved the e-professional development of staff at a Further Education (FE) college (Hramiak, 2004). Starting with the raw data sets, a distillation process for the data is described. This is followed by an explanation of how the data sets are examined for common themes. One of the major challenges facing the e-learning researcher is how to analyze the electronic data such as discussion board messages and emails, and then how to understand the implications of this analysis for teaching and learning. Such analysis enables researchers to act upon the situation in order to improve it for the learners, as well as for themselves (Lally, 2000). This is particularly challenging when the messages are not only numerous, in the region of hundreds or even thousands, for a specific research study, but also because they can be both very complicated and very lengthy. Although tools for analyzing communication patterns have been developed in other disciplines, for example in applied linguistics, they are generally based upon the analysis of large bodies of text. They also involve relatively complex and cumbersome methods, and they are not designed for action research use in the immediacy of particular teaching and learning situations (Lally) such as those for which this article is aimed at – namely those in which students/participants are constantly messaging in real time synchronously and asynchronously. Moreover, such tools are not intentionally designed to analyze dynamic, ongoing collaborative and social situations where knowledge is actively being co-constructed by the participants (Lally)

NCL++: Nested Collaborative Learning for Long-Tailed Visual Recognition

Long-tailed visual recognition has received increasing attention in recent years. Due to the extremely imbalanced data distribution in long-tailed learning, the learning process shows great uncertainties. For example, the predictions of different experts on the same image vary remarkably despite the same training settings. To alleviate the uncertainty, we propose a Nested Collaborative Learning (NCL++) which tackles the long-tailed learning problem by a collaborative learning. To be specific, the collaborative learning consists of two folds, namely inter-expert collaborative learning (InterCL) and intra-expert collaborative learning (IntraCL). In-terCL learns multiple experts collaboratively and concurrently, aiming to transfer the knowledge among different experts. IntraCL is similar to InterCL, but it aims to conduct the collaborative learning on multiple augmented copies of the same image within the single expert. To achieve the collaborative learning in long-tailed learning, the balanced online distillation is proposed to force the consistent predictions among different experts and augmented copies, which reduces the learning uncertainties. Moreover, in order to improve the meticulous distinguishing ability on the confusing categories, we further propose a Hard Category Mining (HCM), which selects the negative categories with high predicted scores as the hard categories. Then, the collaborative learning is formulated in a nested way, in which the learning is conducted on not just all categories from a full perspective but some hard categories from a partial perspective. Extensive experiments manifest the superiority of our method with outperforming the state-of-the-art whether with using a single model or an ensemble. The code will be publicly released.Comment: arXiv admin note: text overlap with arXiv:2203.1535

Peer Collaborative Learning for Online Knowledge Distillation

Traditional knowledge distillation uses a two-stage training strategy to transfer knowledge from a high-capacity teacher model to a compact student model, which relies heavily on the pre-trained teacher. Recent online knowledge distillation alleviates this limitation by collaborative learning, mutual learning and online ensembling, following a one-stage end-to-end training fashion. However, collaborative learning and mutual learning fail to construct an online high-capacity teacher, whilst online ensembling ignores the collaboration among branches and its logit summation impedes the further optimisation of the ensemble teacher. In this work, we propose a novel Peer Collaborative Learning method for online knowledge distillation, which integrates online ensembling and network collaboration into a unified framework. Specifically, given a target network, we construct a multi-branch network for training, in which each branch is called a peer. We perform random augmentation multiple times on the inputs to peers and assemble feature representations outputted from peers with an additional classifier as the peer ensemble teacher. This helps to transfer knowledge from a high-capacity teacher to peers, and in turn further optimises the ensemble teacher. Meanwhile, we employ the temporal mean model of each peer as the peer mean teacher to collaboratively transfer knowledge among peers, which helps each peer to learn richer knowledge and facilitates to optimise a more stable model with better generalisation. Extensive experiments on CIFAR-10, CIFAR-100 and ImageNet show that the proposed method significantly improves the generalisation of various backbone networks and outperforms the state-of-the-art methods

Joint Topic-Semantic-aware Social Recommendation for Online Voting

Online voting is an emerging feature in social networks, in which users can express their attitudes toward various issues and show their unique interest. Online voting imposes new challenges on recommendation, because the propagation of votings heavily depends on the structure of social networks as well as the content of votings. In this paper, we investigate how to utilize these two factors in a comprehensive manner when doing voting recommendation. First, due to the fact that existing text mining methods such as topic model and semantic model cannot well process the content of votings that is typically short and ambiguous, we propose a novel Topic-Enhanced Word Embedding (TEWE) method to learn word and document representation by jointly considering their topics and semantics. Then we propose our Joint Topic-Semantic-aware social Matrix Factorization (JTS-MF) model for voting recommendation. JTS-MF model calculates similarity among users and votings by combining their TEWE representation and structural information of social networks, and preserves this topic-semantic-social similarity during matrix factorization. To evaluate the performance of TEWE representation and JTS-MF model, we conduct extensive experiments on real online voting dataset. The results prove the efficacy of our approach against several state-of-the-art baselines.Comment: The 26th ACM International Conference on Information and Knowledge Management (CIKM 2017