Generating Feedback Reports for Adults Taking Basic Skills Tests

SkillSum is an Artificial Intelligence (AI) and Natural Language Generation (NLG) system that produces short feedback reports for people who are taking online tests which check their basic literacy and numeracy skills. In this paper, we describe the SkillSum system and application, focusing on three challenges which we believe are important ones for many systems which try to generate feedback reports from Web-based tests: choosing content based on very limited data, generating appropriate texts for people with varied levels of literacy and knowledge, and integrating the web-based system with existing assessment and support procedures

SkillSum: basic skills screening with personalised, computer-generated feedback

We report on our experiences in developing and evaluating a system that provided formative assessment of basic skills and automatically generated personalised feedback reports for 16-19 year-old users. Development of the system was informed by literacy and numeracy experts and it was trialled 'in the field' with users and basicskills tutors. We experimented with two types of assessment and with feedback that evolved from long, detailed reports with graphics to more readable, shorter ones with no graphics. We discuss the evaluation of our final solution and compare it with related systems

Deriving content selection rules from a corpus of non-naturally occurring documents for a novel NLG application

We describe a methodology for deriving content selection rules for NLG applications that aim to replace oral communications from human experts by written communications that are generated automatically. We argue for greater involvement of users and for a strategy for handling sparse data

Braintree College: report from the Inspectorate (FEFC inspection report; 92/95 and 17/99)

Comprises two Further Education Funding Council (FEFC) inspection reports for the periods 1994-95 and 1998-99

Three Approaches to Generating Texts in Different Styles

Natural Language Generation (nlg) systems generate texts in English and other human languages from non-linguistic input data. Usually there are a large number of possible texts that can communicate the input data, and nlg systems must choose one of these. We argue that style can be used by nlg systems to choose between possible texts, and explore how this can be done by (1) explicit stylistic parameters, (2) imitating a genre style, and (3) imitating an individual’s style

Experiments with discourse-level choices and readability

This paper reports on pilot experiments that are being used, together with corpus analysis, in the development of a Natural Language Generation (NLG) system, GIRL (Generator for Individual Reading Levels). GIRL generates reports for individuals after a literacy assessment. We tested GIRL's output on adult learner readers and good readers. Our aim was to find out if choices the system makes at the discourse-level have an impact on readability. Our preliminary results indicate that such choices do indeed appear to be important for learner readers. These will be investigated further in future larger-scale experiments. Ultimately we intend to use the results to develop a mechanism that makes discourse-level choices that are appropriate for individuals' reading skills

Chippenham College (FEFC inspection report; 27/95 and 20/98)

Comprises two Further Education Funding Council (FEFC) inspection reports for the periods 1994-95 (27/95), and 1997-98 (20/98). The FEFC has a legal duty to make sure further education in England is properly assessed. Inspections and reports on each college of further education are conducted according to a four-year cycle. Chippenham College is a general further education college serving the education and training needs of market towns and rural villages in north-west Wiltshire