AN INVESTIGATION ABOUT TRANSLATION AND INTERPRETATION OF STATISTICAL GRAPHS AND TABLES BY STUDENTS OF PRIMARY EDUCATION
Bibliographic record
Abstract
This work used a test to explore capacities, limita tions and errors that students may have during processes of learning statistical graphs in Primary Education. We display some results of a test given to groups of students from schools in New Zea land and Spain, to investigate how they make translations between different types of graphic rep resentation . ANTECEDENTS TO OUR INVESTIGATION People accept that as time passes society’s way of life requires citizens to have some knowledge of statistics in order to understand bett er their environment and to exercise their rights. Statistical graphs are shown very often in scientif ic articles and are a common way of social communication; this is the reason for their inclusi on as an important part of curricula in compulsory education. We question, as teachers, whether the usual curriculum content on statistical graphs is in fact enough for students t o understand, for example, information given in graphs appearing in the media. Some researchers in statistics have investigated th e theory of the construction and perception of graphs. Cleveland and McGill (1984) give a list of basic perceptive elements, useful in the reading and understanding of graphs, such as scales, shadows, shapes or areas, and show a hierarchic ordering of them. However, even now ther e is not enough work done on the design and good use of graphs. A theory of graphic methods, ab out how different types of graphs are selected, made or compared, is necessary; even if c ommon sense and intuition play an important role on it. Research in mathematics education is endeavouring t o find out what statistical knowledge primary teachers need, what they need to teach and how. At present, most school curricula require that students must construct and understand tables, bar and sector charts, histograms and frequency polygons. It is also known that many teac hers need to improve their knowledge of statistics and its didactical aspects, including ta king into account difficulties and errors experienced by students (Batanero et al ., 1994). We underline one work on Statistics Education about critical factors that have to do with graphical comprehension and its instructional impli cations (Friel et al ., 2001). It shows a compilation of research about making and using stat istical graphs, detecting those factors that influence comprehension, and suggests some features that should to be considered for further investigations. Related to graph comprehension, and having to do wi th the “alphabetization capacity” or capacity to use written information to advance ours elves in our society, this work describes three behaviours: translation, comprehension and extrapol ation/interpolation. Three different levels have been identified for the se behaviours in the process of graphical comprehension: an elementary level, with preference to data extraction from a graph; an intermediate level, orientated to interpolation and finding out data relations showed in graphs and; an advanced level that includes data extrapolation and analysis of relations implicit in graphs. With the present work, we use a questionnaire that was designed to work at the intermediate level, with the aim to analyze which k inds of behaviours ten to twelve years old children use in the process of making and comprehension of graphs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".