Open-Ended Questions (Version 2.0)

Two aspects of open-ended survey questions are addressed in this contribution. The first aspect is the fielding of such questions: When, and for what purpose, are they useful? Who answers such questions, in the first place? And what should be taken into account when developing and designing open-ended questions? The second part of the article shows possible ways of evaluating open-ended questions. These include content analysis, which has a long tradition in the evaluation of open-ended questions. In addition, computer-supported, dictionary-based content analysis plays a major role as it is especially suitable for the analysis of responses to open questions because they are, as a rule, short and limited by the context of the question. Co-occurrence analysis, which can yield an overall picture of the responses, is a relatively new way of evaluating open-ended questions

Open-Ended Questions in Reading

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Open-ended questions

This writer has a mobile phone, which when unlocked, displays the question, “How are you today?” This is one of the simplest examples of an open-ended question. We can think of other such examples, such as: “What did you feel after reading this article?”, “How interesting was your mathematics class yesterday?” These are in direct contrast with what are known as “closed-ended” questions, such as “What is the colour of the silk-sari you purchased yesterday?” “How much increment did you receive in your salary?

Supporting mediated peer-evaluation to grade answers to open-ended questions

We show an approach to semi-automatic grading of answers given by students to open ended questions (open answers). We use both peer-evaluation and teacher evaluation. A learner is modeled by her Knowledge and her assessments quality (Judgment). The data generated by the peer- and teacher- evaluations, and by the learner models is represented by a Bayesian Network, in which the grades of the answers, and the elements of the learner models, are variables, with values in a probability distribution. The initial state of the network is determined by the peer-assessment data. Then, each teacher’s grading of an answer triggers evidence propagation in the network. The framework is implemented in a web-based system. We present also an experimental activity, set to verify the effectiveness of the approach, in terms of correctness of system grading, amount of required teacher's work, and correlation of system outputs with teacher’s grades and student’s final exam grade

Panel data and open-ended questions: Understanding perceptions of quality of life

This paper describes the burgeoning interest in quality of life studies and suggests that as well as expert definitions, we need to consider people’s own perceptions of what matters. Using openended questions from the 1997 and 2002 waves of the British Household Panel Survey we analyse both quantitatively and qualitatively how perceptions of quality of life differ for men and women across the life course. Qualitative analysis reveals that key domains such as health, family and finances often refer, not to self, but to others. Longitudinal analysis demonstrates that people’s perceptions of quality of life change over time, particularly before and after important life transitions. Thus our findings challenge overly individualistic and static conceptions of quality of life and reveal quality of life as a process, not a fixed state

Automated classification for open-ended questions with BERT

Manual coding of text data from open-ended questions into different categories is time consuming and expensive. Automated coding uses statistical/machine learning to train on a small subset of manually coded text answers. Recently, pre-training a general language model on vast amounts of unrelated data and then adapting the model to the specific application has proven effective in natural language processing. Using two data sets, we empirically investigate whether BERT, the currently dominant pre-trained language model, is more effective at automated coding of answers to open-ended questions than other non-pre-trained statistical learning approaches. We found fine-tuning the pre-trained BERT parameters is essential as otherwise BERT's is not competitive. Second, we found fine-tuned BERT barely beats the non-pre-trained statistical learning approaches in terms of classification accuracy when trained on 100 manually coded observations. However, BERT's relative advantage increases rapidly when more manually coded observations (e.g. 200-400) are available for training. We conclude that for automatically coding answers to open-ended questions BERT is preferable to non-pretrained models such as support vector machines and boosting

Efektivitas Metode Diskusi Dengan Pendekatan Open-Ended Questions untuk Meningkatkan Hasil Belajar Fisika Siswa Kelas X SMA Muhammadiyah Kota Tegal

Tujuan dari penelitian ini untuk (1) mengetahui hasil belajar fisika menggunakan metode diskusi dengan pendekatan open-ended questions ditinjau dari keterampilan berpikir kritis siswa (2) mengetahui perbedaan efektivitas pembelajaran fisika menggunakan metode diskusi dengan pendekatan open-ended questions dan pembelajaran fisika tanpa menggunakan metode diskusi dengan pendekatan open-ended questions ditinjau dari keterampilan berpikir kritis siswa. Jenis penelitian ini adalah quasi exsperiment dengan desain pretest-posttest control group design. Teknik pengumpulan data yang digunakan adalah dengan tes. Instrumen penelitian yang digunakan  adalah RPP, LKS, Soal Pretes dan Posttest. Hasil penelitian ini antara lain (1) hasil belajar fisika menggunakan metode diskusi dengan pendekatan open-ended questions lebih baik daripada hasil belajar fisika tanpa menggunakan metode diskusi dengan pendekatan open-ended questionsditinjau dari keterampilan berpikir kritis siswa. (2) terdapat perbedaan efektivitas dari pembelajaran fisika menggunakan metode diskusi dengan pendekatan open-ended questions dan pembelajaran fisika tanpa menggunakan metode diskusi dengan pendekatan open-ended questionsditinjau dari keterampilan berpikir kritis siswa

Using Spaced Learning Principles to Translate Knowledge into Behavior: Evidence from Investigative Interviews of Alleged Child Abuse Victims

The present study assessed the progress of 13 investigative interviewers (child protection workers and police officers) before, during, and after an intensive training program (n = 132 interviews). Training began with a 2-day workshop covering the principles of child development and child-friendly interviewing. Interviewers then submitted interviews on a bi-weekly basis to which they received written and verbal feedback over an 8-month period. A refresher session took place two months into training. Interestingly, improvements were observed only after the refresher session. Interviews conducted post-refresher training contained proportionally more open-ended questions, more child details in response to open-ended questions, and proportionally fewer closed questions than interviews conducted prior to training and in the first half of the training program. The need for ‘spaced learning’ may underlie why so many training programs have had little effect on practice