A Perspective on Computer Assisted Assessment Techniques for Short Free-Text Answers

Computer Assisted Assessment (CAA) has been existing for several years now. While some forms of CAA do not require sophisticated text understanding (e.g., multiple choice questions), there are also student answers that consist of free text and require analysis of text in the answer. Research towards the latter till date has concentrated on two main sub-tasks: (i) grading of essays, which is done mainly by checking the style, correctness of grammar, and coherence of the essay and (ii) assessment of short free-text answers. In this paper, we present a structured view of relevant research in automated assessment techniques for short free-text answers. We review papers spanning the last 15 years of research with emphasis on recent papers. Our main objectives are two folds. First we present the survey in a structured way by segregating information on dataset, problem formulation, techniques, and evaluation measures. Second we present a discussion on some of the potential future directions in this domain which we hope would be helpful for researchers.

Primary structure of a Thomsen-Friedenreich-antigen-specific lectin, jacalin [Artocarpus integrifolia (jack fruit) agglutinin]. Evidence for the presence of an internal repeat

Jacalin [Artocarpus integrifolia (jack fruit) agglutinin] is made up of two types of chains, heavy and light, with M(r) values of 16,200 +/- 1200 and 2090 +/- 300 respectively (on the basis of gel-permeation chromatography under denaturing conditions). Its complete amino acid sequence was determined by manual degradation using a 4-dimethylaminoazobenzene 4'-isothiocyanate double-coupling method. Peptide fragments for sequence analysis were obtained by chemical cleavages of the heavy chain with CNBr, hydroxylamine hydrochloride and iodosobenzoic acid and enzymic cleavage with Staphylococcus aureus proteinase. The peptides were purified by a combination gel-permeation and reverse-phase chromatography. The light chains, being only 20 residues long, could be sequenced without fragmentation. Amino acid analyses and carboxypeptidase-Y-digestion C-terminal analyses of the subunits provided supportive evidence for their sequence. Computer-assisted alignment of the jacalin heavy-chain sequence failed to show sequence similarity to that of any lectin for which the complete sequence is known. Analyses of the sequence showed the presence of an internal repeat spanning residues 7-64 and 76-130. The internal repeat was found to be statistically significant.

A mode-shape classification technique for robust speech rate estimation and syllable nuclei detection

Acoustic feature based speech (syllable) rate estimation and syllable nuclei detection are important problems in automatic speech recognition (ASR), computer assisted language learning (CALL) and fluency analysis. A typical solution for both the problems consists of two stages. The first stage involves computing a short-time feature contour such that most of the peaks of the contour correspond to the syllabic nuclei. In the second stage, the peaks corresponding to the syllable nuclei are detected. In this work, instead of the peak detection, we perform a mode-shape classification, which is formulated as a supervised binary classification problem - mode-shapes representing the syllabic nuclei as one class and remaining as the other. We use the temporal correlation and selected sub-band correlation (TCSSBC) feature contour and the mode-shapes in the TCSSBC feature contour are converted into a set of feature vectors using an interpolation technique. A support vector machine classifier is used for the classification. Experiments are performed separately using Switchboard, TIMIT and CTIMIT corpora in a five-fold cross validation setup. The average correlation coefficients for the syllable rate estimation turn out to be 0.6761, 0.6928 and 0.3604 for three corpora respectively, which outperform those obtained by the best of the existing peak detection techniques. Similarly, the average F-scores (syllable level) for the syllable nuclei detection are 0.8917, 0.8200 and 0.7637 for three corpora respectively. (C) 2016 Elsevier B.V. All rights reserved.