EChO, the Exoplanet Characterisation Observatory, has been one of the five M-class mission candidates competing for the M3 launch slot within the science programme Cosmic Vision 2015-2025 of the European Space Agency (ESA). As such, EChO has been the subject of a Phase 0/A study that involved European Industry, research institutes and universities from ESA member states and that concluded in Exp Astron EChO is a concept for a dedicated mission to measure the chemical composition and structure of hundreds of exoplanet atmospheres using the technique of transit spectroscopy. With simultaneous and uninterrupted spectral coverage from the visible to infrared wavelengths, EChO targets extend from gas giants (Jupiter or Neptune-like) to super-Earths in the very hot to temperate zones of F to M-type host stars, opening up the way to large-scale, comparative planetology that would place our own solar system in the context of other planetary systems in the Milky Way. A review of the performance requirements of the EChO mission was held at ESA at the end of 2013, with the objective of assessing the readiness of the mission to progress to the Phase B1 study phase. No critical issues were identified from a technical perspective, however a number of recommendations were made for future work. Since the mission was not selected for the M3 launch slot, EChO is no longer under study at ESA. In this paper we give an overview of the final mission concept for EChO as of the end of the study, from scientific, technical and operational perspectives.

In the present study, we tested a computer-based procedure for assessing very concise summaries (50 words long) of two types of text (narrative and expository) using latent semantic analysis (LSA) in comparison with the judgments of four human experts. LSA was used to estimate semantic similarity using six different methods: four holistic (summary-text, summary-summaries, summary-expert summaries, and pregraded-ungraded summary) and two componential (summary-sentence text and summary-main sentence text). A total of 390 Spanish middle and high school students (14-16 years old) and six experts read a narrative or expository text and later summarized it. The results support the viability of developing a computerized assessment tool using human judgments and LSA, although the correlation between human judgments and LSA was higher in the narrative text than in the expository, and LSA correlated more with human content ratings than with human coherence ratings. Finally, the holistic methods were found to be more reliable than the componential methods analyzed in this study.

Background The present study analysed how relevance instructions affect eye movement patterns and the performance in a summary task of six expository texts. Methods Forty‐one undergraduate students participated in the experiment; half of them were instructed to make an oral summary of the main ideas focusing on the ‘why’ question that appeared at the end of the first paragraph (specific relevance instruction), while the other half were instructed to make an oral summary of the main ideas of the text (general relevance instruction). Results Eye movement patterns revealed that specific instructions promoted more and longer fixations and more regressions for relevant information than general instructions. A higher percentage of words in the summary task related to relevant information was recalled when readers received specific instructions. Conclusions These findings suggest that relevance instructions influence how readers enact strategies to meet their reading goals and how these strategies are reflected on memory.

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ARIEL, the Atmospheric Remote sensing Infrared Exoplanet Large survey, is one of the three M-class mission candidates competing for the M4 launch slot within the Cosmic Vision science programme of the European Space Agency (ESA). As such, ARIEL has been the subject of a Phase A study that involved European industry, research institutes and universities from ESA member states. This study is now completed and the M4 down-selection is expected to be concluded in November 2017. ARIEL is a concept for a dedicated mission to measure the chemical composition and structure of hundreds of exoplanet atmospheres using the technique of transit spectroscopy. ARIEL targets extend from gas giants (Jupiter or Neptune-like) to super-Earths in the very hot to warm zones of F to M-type host stars, opening up the way to large-scale, comparative planetology that would place our own Solar System in the context of other planetary systems in the Milky Way. A technical and programmatic review of the ARIEL mission was performed between February and May 2017, with the objective of assessing the readiness of the mission to progress to the Phase B1 study. No critical issues were identified and the mission was deemed technically feasible within the M4 programmatic boundary conditions. In this paper we give an overview of the final mission concept for ARIEL as of the end of the Phase A study, from scientific, technical and operational perspectives.

In this paper, we present several proposals in order to improve the LSA tools to evaluate brief summaries (less than 50 words) of narrative and expository texts. First, we analyse the quality of six different methods assessing essays that have been widely employed before (Foltz et al., 2000). The second objective is to analyse how new algorithms inspired by some authors (Denhière et al., 2007) that try to emulate human behaviour to improve the reliability of LSA with human graders when assessing short summaries, compared with standard LSA use in expository text. Finally, we present an assessment method to combine LSA as a semantic computational linguistic model with ROUGE-N as a lexical model, to show how combining different automatic evaluation systems (LSA and ROUGE) can improve the quality of assessments in different academic levels.

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Reading comprehension involves a reader developing a mental representation of a text through the establishment of causal relations based on the ideas and events in the text. This is especially relevant to scientific text comprehension. Causal relations are fundamental to the process of comprehension as they provide a framework or scaffolding to order information in a logical way that is consistent with the argument. The most common method of assessing comprehension is based on the reader answering a series of multiple choice questions. It is unusual for comprehension measures to use an open task such as a summary. However, summaries require the reader to use writing skills as well as those of comprehension, thus revealing wide individual differences among students. This gives rise to two questions: (a) up to what point is a summary a reflection of the causal structure of a text, and (b) what-if any-is the influence of the causal relations on the comprehension of more competent and less competent readers? In this chapter we analyze the causal structure of scientific texts, as opposed to that of narratives, and explore how high school students process and comprehend these causal relations. We also examine how students' comprehension of causal relations can be evaluated by multiple choice tasks or open tasks such as summaries. Finally, we discuss some educational implications for improving comprehension in science.