ASSESSING STUDENT LEARNING WITH AUTOMATED TEXT PROCESSING TECHNIQUES

Research on distance learning and computer-aided grading has been developed in parallel. Little work has been done in the past to join the two areas to solve the problem of automated learning assessment in virtual classrooms. This paper presents a model for learning assessment using an automated text processing technique to analyze class messages with an emphasis on course topics produced in an online class. It is suggested that students should be evaluated on many dimensions, including the learning artifacts such as course work submitted and class participation. Taking all these grading criteria into consideration, we design a model which combines three grading factors: the quality of course work, the quantity of efforts, and the activeness of participation, for evaluating the performance of students in the class. These three main items are measured on the basis of keyword contribution, message length, and message count, and a score is derived from the class messages to evaluate students’ performance. An assessment model is then constructed from these three measures to compute a performance indicator score for each student. The experiment shows that there is a high correlation between the performance indicator scores and the actual grades assigned by instructors. The rank orders of students by performance indicator scores and by the actual grades are highly correlated as well. Evidence from the experiment shows that the computer grader can be a great supplementary teaching and grading tool for distance learning instructors

Search Personalization: Knowledge-Based Recommendation in Digital Libraries

Recommendation engines have made great strides in understanding and implementing search personalization techniques to provide interesting and relevant documents to users. The latest research effort advances a new type of recommendation technique, Knowledge Based (KB) engines, that strive to understand the context of the user’s current information need and then filter information accordingly. The KB engine proposed in this paper requires less effort from the user in representing the search task and is the first of its kind implemented in a digital library setting. The KB engine performance was compared with Content Based (CB) and Collaborative Filtering (CF) recommendation techniques and the text search engine Lucene by asking sixty subjects to perform two different tasks to find relevant documents in a database of 212,000 documents from 22 National Science Digital Library (NSDL) collections. Our KB engine design outperforms CB, CF, and text search techniques in nearly all areas of evaluation

Automatically Finding Significant Topical Terms from Documents

With the pervasion of digital textual data, text mining is becoming more and more important to deriving competitive advantages. One factor for successful text mining applications is the ability of finding significant topical terms for discovering interesting patterns or relationships. Document keyphrases are phrases carrying the most important topical concepts for a given document. In many applications, keyphrases as textual elements are better suited for text mining and could provide more discriminating power than single words. This paper describes an automatic keyphrase identification program (KIP). KIP’s algorithm examines the composition of noun phrases and calculates their scores by looking up a domain-specific glossary database; the ones with higher scores are extracted as keyphrases. KIP’s learning function can enrich its glossary database by automatically adding new identified keyphrases. KIP’s personalization feature allows the user build a glossary database specifically suitable for the area of his/her interest

Extracting Conceptual Terms from Medical Documents

Automated biomedical concept recognition is important for biomedical document retrieval and text mining research. In this paper, we describe a two-step concept extraction technique for documents in biomedical domain. Step one includes noun phrase extraction, which can automatically extract noun phrases from medical documents. Extracted noun phrases are used as concept term candidates which become inputs of next step. Step two includes keyphrase extraction, which can automatically identify important topical terms from candidate terms. Experiments were conducted to evaluate results of both steps. The experiment results show that our noun phrase extractor is effective in identifying noun phrases from medical documents, so is the keyphrase extractor in identifying document conceptual terms

Generating Better Concept Hierarchies Using Automatic Document Classification

ABSTRACT This paper presents a hybrid concept hierarchy development technique for web returned documents retrieved by a meta-search engine. The aim of the technique is to separate the initial retrieved documents into topical oriented categories, prior to the actual concept hierarchy generation. The topical categories correspond to different semantic aspects of the query. This is done using a 1-of-n automatic document classification, on the initial set of returned documents. Then, an individual topical concept hierarchy is automatically generated inside each of the resulted categories. Both steps are executed on the fly at retrieval time. Due to the efficiency constraints imposed by the web retrieval context, the algorithm only uses document snippets (rather than full web pages) for both document classification and concept hierarchy generation. Experimental results show that the algorithm is able to improve the quality of the concept hierarchy presented to the searcher; at the same time, the efficiency parameters are kept within reasonable intervals

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Abstract Identifying important concepts from medical documents

Automated medical concept recognition is important for medical informatics such as medical document retrieval and text mining research. In this paper, we present a software tool called keyphrase identification program (KIP) for identifying topical concepts from medical documents. KIP combines two functions: noun phrase extraction and keyphrase identification. The former automatically extracts noun phrases from medical literature as keyphrase candidates. The latter assigns weights to extracted noun phrases for a medical document based on how important they are to that document and how domain specific they are in the medical domain. The experimental results show that our noun phrase extractor is effective in identifying noun phrases from medical documents, so is the keyphrase extractor in identifying important medical conceptual terms. They both performed better than the systems they were compared to

Understanding Students’ Perceptions of an Automated Feedback System: an Empirical Study Based on UTAUT

Formative feedback has long been recognized as a crucial scaffold for student learning. There is an increasing interest in developing better strategies to provide students with automated feedback to scaffold their learning. In this research, an automated feedback system was developed to support student learning of conceptual knowledge in the course of writing assignments. In the proposed system, formative feedback is generated automatically with the help of concept maps constructed from instructors’ lecture slides and students’ writing assignments. The primary goal of this empirical study was to understand students’ perceptions of the proposed automated feedback system employing the model of Unified Theory of Acceptance and Use of Technology (UTAUT). The preliminary results of the study show that the majority of the students (about 80%) perceived that the system is helpful for improving their coursework and would recommend doing the assignment with the proposed system for future classes

Assessing Student Learning with Automated Text Processing Techniques ASSESSING STUDENT LEARNING WITH AUTOMATED TEXT PROCESSING TECHNIQUES

Research on distance learning and computer-aided grading has been developed in parallel. Little work has been done in the past to join the two areas to solve the problem of automated learning assessment in virtual classrooms. This paper presents a model for learning assessment using an automated text processing technique to analyze class messages with an emphasis on course topics produced in an online class. It is suggested that students should be evaluated on many dimensions, including the learning artifacts such as course work submitted and class participation. Taking all these grading criteria into consideration, we design a model which combines three grading factors: the quality of course work, the quantity of efforts, and the activeness of participation, for evaluating the performance of students in the class. These three main items are measured on the basis of keyword contribution, message length, and message count, and a score is derived from the class messages to evaluate students ’ performance. An assessment model is then constructed from these three measures to compute a performance indicator score for each student. The experiment shows that there is a high correlation between the performance indicator scores and the actual grades assigned by instructors. The rank orders of students by performance indicator scores and by the actual grades are highly correlated as well. Evidence from the experiment shows that the computer grader can be a great supplementary teaching and grading tool for distance learning instructors