Automatic scoring of short handwritten essays in reading comprehension tests

The AES system is based on the approach of latent semantic analysis where a set of human-scored answers are used to determine scoring system parameters using a machine learning approach.

Heuristics derived from reading comprehension research are employed to obtain additional scoring features. Handwriting recognition is based on a fusion of analytic and holistic methods together with contextual processing based on trigrams.

Handwritten essays are widely used in educational assessments, particularly in classroom instruction.

End-to-end performance results are not far from automatic scoring based on perfect manual transcription, thereby demonstrating that handwritten essay scoring has practical potential. The first is based on latent semantic analysis, which requires a reasonable level of handwriting recognitio Year: The second uses an artificial neural network ANN which is based on features extracted from the handwriting image.

Previous article in issue. The paper describes computational methods of scoring such responses using handwriting recognition and automatic essay scoring technologies. The lexicons to recognize handwritten words are derived from the reading passage, the testing prompt, answer rubric and student responses.

Under an Elsevier user license open archive Abstract Reading comprehension is largely tested in schools using handwritten responses. Handwriting recognition is based on a fusion of analytic and holistic methods together with contextual processing based on trigrams.

Reading comprehension is largely tested in schools using handwritten responses.

LSA requires the use of a large lexicon for recognizing the entire response whereas ANN only requires a small lexicon to populate its features thereby making it practical with current word recognition technology. The OHR system performs several pre-processing steps such as forms removal, rule-line removal and segmentation of text lines and words.

Document image-level operations include: Sorry, we are unable to provide the full text but you may find it at the following location s: Results with two methods of essay scoring— both of which are based on learning from a human-scored set — are described.

The lexicons to recognize handwritten words are derived from the reading passage, the testing prompt, answer rubric and student responses. The final recognition step, which is tuned to the task of reading comprehension evaluation in a primary education setting, is performed using a lexicon derived from the passage to be read.

The approaches are based on coupling methods of document image analysis and recognition together with those of automated essay scoring. Document image-level operations include: The first is based on latent semantic analysis LSAwhich requires a reasonable level of handwriting recognition performance.

The system is based on integrating the two technologies of optical handwriting recognition OHR and automated essay scoring AES. Testing on a small set of handwritten answers indicate that system performance is comparable to that of automatic scoring based on manual transcription.

System performance is compared to scoring done by human raters. The goal is to assign to each handwritten response a score which is comparable to that of a human scorer even though machine handwriting recognition methods have high transcription error rates.

A test-bed of essays written in response to prompts in statewide reading comprehension tests and scored by humans is used to train and evaluate the methods.Automatic Scoring of Handwritten Essays Using Latent Semantic Analysis. In H.

Bunke, and L. Spitz (Eds.), Document Analysis Systems (pp. 71–83). New Zealand: Springer Nelson. Automating the task of scoring short handwritten student essays is considered. The goal is to as- scoring.

The test-bed is that of essays written by children in reading comprehension tests.

The pro-cess involves several image-level operations: re-movalof pre-printedmatter, segmentationof hand- On the Automatic Scoring of Handwritten Essays.

Solving the problem also promises to reduce costs and raise efficiency of large-scale assessments. This is an interdisciplinary project involving three distinct knowledge areas: optical handwriting recognition (OHR), automatic essay scoring (AES) and reading comprehension studies.

The system is based on integrating the two technologies of optical handwriting recognition (OHR) and automated essay scoring (AES).

The OHR system performs several pre-processing steps such as forms removal, rule-line removal and segmentation of text lines and words. Short-answer questions (written or spoken presentation and response), e.g.

reading comprehension, science concepts, multisource information synthesis, 5. Oral reading fluency (rate, accuracy, and expression). Examples of these operational item types alone and in combination are briefly described in the following section. The lexicons to recognize handwritten words are derived from the reading passage, the testing prompt, answer rubric and student responses.

Recognition methods utilize children’s handwriting styles. Heuristics derived from reading comprehension research.

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Automatic scoring of short handwritten essays in reading comprehension tests
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