In this paper, we propose a counterfactual explanations-based approach to provide an automatic and intelligent recommendation that supports student's self-regulation of learning in a data-driven manner, aiming to improve their performance in courses. Existing work under the fields of learning analytics and AI in education predict students' performance and use the prediction outcome as feedback without explaining the reasons behind the prediction. Our proposed approach developed an algorithm that explains the root causes behind student's performance decline and generates data-driven recommendations for action. The effectiveness of the proposed predictive model that constitutes the intelligent recommendations is evaluated, with results demonstrating high accuracy.

The advancements in learning analytics and artificial intelligence have shown potential to transform traditional modalities of education. One such advancement relates to the use of educational data to track students’ knowledge state [1] . In the field of Artificial Intelligence in Education knowledge tracing is a well-established area where a machine models the students’ knowledge as they interact with coursework. Effective modeling of student knowledge can have a high impact on the provision of adaptive learning. In fact, lately, research on knowledge tracing is intensifying with a particular focus on the utilisation of new machine learning algorithms for modelling the students’ knowledge levels and for the prediction of performance on future tasks and assessment questions [2] . In the case of question-level assessment, knowledge tracing provides an interpretation of the learner’s current knowledge level and models their mastery of the skill or knowledge component to which future questions are related [3] .

This study describes an approach for mining events preceding a cervical cancer diagnosis from health records.

This paper describes a set of methods to link entities across images and text.As a corpus, we used a data set of images, where each image is commented by a short caption and where the regions in the images are manually segmented and labeled with a category.We extracted the entity mentions from the captions and we computed a semantic similarity between the mentions and the region labels.We also measured the statistical associations between these mentions and the labels and we combined them with the semantic similarity to produce mappings in the form of pairs consisting of a region label and a caption entity.In a second step, we used the syntactic relationships between the mentions and the spatial relationships between the regions to rerank the lists of candidate mappings.To evaluate our methods, we annotated a test set of 200 images, where we manually linked the image regions to their corresponding mentions in the captions.Eventually, we could match objects in pictures to their correct mentions for nearly 89 percent of the segments, when such a matching exists.

In this paper we investigate the problem of segmenting images using the information in text annotations. In contrast to the general image understanding problem, this type of annotation guided segmentation is less ill-posed in the sense that for the output there is higher consensus among human annotations. In the paper we present a system based on a combined visual and semantic pipeline. In the visual pipeline, a list of tentative figure-ground segmentations is first proposed. Each such segmentation is classified into a set of visual categories. In the natural language processing pipeline, the text is parsed and chunked into objects. Each chunk is then compared with the visual categories and the relative distance is computed using the word-net structure. The final choice of segments and their correspondence to the chunked objects are then obtained using combinatorial optimization. The output is compared to manually annotated ground-truth images. The results are promising and there are several interesting avenues for continued research.