Informed by these theories of linguistics and learning, Functional Grammar Analysis is a curriculum for elementary school teachers to be used in the context of language arts, Palincsar explained. It is one tool to help teachers use text and learn to read with students in ways that engage students in thinking about scientific concepts. This is accomplished by focusing detailed attention on the language.
The curriculum involves interactive reading and discussion of text, first-hand investigations, demonstrations of phenomena, and support for writing about the phenomena. Palincsar noted the effectiveness of this curriculum with a diverse group of students has been supported through research Palincsar et al. Over 90 percent of children in these participating classrooms were bilingual, with a high proportion classified as English language learners, and over 90 percent of the students in the schools in their research qualified for free and reduced-cost lunch.
In addition, analysis of student writing showed an increase, on average, of five idea units from the pre to postwriting assessment, an increase in the range of ideas children included in their explanations, and more use of writing with connectors and author attitude. With Functional Grammar Analysis , Palincsar explained, teachers address the technical nature of science texts by helping students identify certain patterns in the language.
Using these tools, she said, teachers help students see how meanings of technical words become clearer as the text evolves from beginning to end. In these texts in which the purpose is to describe how something happens, the flow of ideas often follows an identifiable pattern. A concept named at the end of one phrase is used at the beginning of the next, and ideas build upon one another. Connections between phrases also have particular meaning in science texts. They can convey present time, cause, condition, contrast, or other linkages.
Examples of these various text patterns are shown in Box When a material has electrons that are able to move very freely, it conducts electricity. We call it a conductor. Most metals are good conductors. The electric current provides energy that makes things run.
The electrons flow through wires that are made of metal conductors and covered in plastic an insulator. The energy of the electrons is converted to heat or light as the electrons make resisters run. Last, Schleppegrell indicated that although science texts can seem objective and impersonal, author word choice conveys a perspective on a range of ideas.
Authors choose words to convey their ideas about certainty or likelihood or their attitude about a concept. Examining these texts for author perspective is part of the process of identifying claims an author makes and the evidence used to support that claim, which encourages students to be critical readers of text.
According to Palincsar , p. Bringing a critical stance toward ideas based on reasoning and learning to engage in scientific argumentation are key elements of scientific discourse, according to two presenters who focused their remarks specifically on the importance of scientific discourse in the classroom.
Sarah Michaels, Clark University, addressed the centrality of discourse as part of the social nature of science. Later in the workshop, Okhee Lee of New York University shared her views on this topic, providing an initial framework for considering the analytic, receptive, and productive language functions that scientific discourse in the classroom require.
That is, students learn how to reason — constructing, engaging in, and critiquing arguments based on evidence — primarily through talk, attention, and shared activities with others. These social activities can include writing in addition to talk, but have as their ultimate aim to make student thinking public and available to the other members of the community. Thus, she said, the challenge becomes creating classroom environments that support this type of structured social interaction and public reasoning.
In fact, Michaels argued that all scientific practices involve these public reasoning practices. As Michaels noted, very little of this type of discourse is currently happening in classrooms today, and typically, teachers use an Initiation-Response-Evaluation approach to classroom discussions Cazden and Mehan, Such discussions involve the teacher asking a question that generally has one right answer, seeking a typically short response from a student, and then evaluating the correctness of that response.
She said this type of discourse is prevalent for a number of reasons. First, most teachers experienced this form of interaction themselves as students. Second, the discussion can be fast-paced, enabling the teacher to cover a lot of material in a relatively short amount of time. Teachers also retain control over the discussion. Although this approach is useful for quick evaluations and checking student knowledge, significant changes are needed to move from this approach to one that promotes reasoning, according to Michaels.
To promote a culture shift to discussions centered around reasoning in classrooms, Michaels stated that teachers need particular forms of support beyond the. Successful strategies to help teachers share three common elements: 1 a framework of shared goals and a set of talk moves and strategies focused on accomplishing those goals; 2 challenging and coherent content to discuss; and 3 collections of video examples of scientific discourse. The impact of reasoning-focused classrooms on student thinking and learning can be significant because teachers must carefully consider content, learning goals and expectations, the cognitive demands of the task, and the knowledge possessed, perceived, and to be learned by students.
Because of their impact and centrality to learning the scientific practices, Michaels argued that they should be the center of science teaching. Overall, she found that while both point to an important role for talk as a scientific practice and as a way to learn content, talk is far less emphasized than is writing.
Discourse appeared to lack a clear definition, she said. Beyond the standards, she noted that students have varying levels of exposure to language used in science and teachers generally do not model the language practices of science. Student writing often mirrors the way they speak. Lee observed that despite these challenges, oral discourse in the classroom can benefit both science and language development.
She argued that science learning is based on experience. Experience, in turn, is essential for the development of oral language. The development of oral language supports written language and is critical to the construction of meaning. Graphic organisers can be used to set purposes for reading, thereby helping to focus student reading and to assist their comprehension.
This note-taking system can also be used to support students to develop scientific understanding, whether when reading texts or listening to a read-aloud. Before r e ading, students divide a page into three unequal sections as shown below. While r eading or listening to a speaker , the student writes ideas and concepts from the reading into the note-taking section. After reading, t he student:. Our website uses a free tool to translate into other languages. This tool is a guide and may not be accurate.
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Page Content. On this page Explicitly teaching text structure Generating text-dependent questions Using graphic organisers In Science, students read for a variety of reasons, including to: develop understanding assess experimental method identify and evaluate evidence determine validity.
They: introduce students to a range of new vocabulary have high lexical density many words that carry content such as nouns or verbs explain abstract concepts and processes require the reader to move between text and images to develop meaning. These include but are not limited to: science textbooks scientific reports newspaper articles material on websites. Reading in Science should be both an active and interactive process. Strategies that teachers can use to support students to read texts include: Explicitly teaching text structure Generating text-dependent questions Using graphic organisers Explicitly teaching text structure A simple strategy to begin supporting students to develop literacy within Science is to explicitly teach them about text structure, organisation and visual supports.
Scientific texts: are often chronologically ordered describe causal effects use headings to organise and categorise sections of text include images and diagrams that support and add meaning to written language. It is important to put the magnitude of these results in context. A FRE score of is designed to reflect the reading level of a to year old.
A score between 0 and 30 is considered understandable by college graduates Flesch, ; Kincaid et al. In other words, more than a fifth of scientific abstracts now have a readability considered beyond college graduate level English. However, the absolute readability scores should be interpreted with some caution: scores can vary due to different media e.
We then validated abstract readability against full text readability, demonstrating that it is a suitable approximation for comparing main texts. We investigated two possible reasons why this trend has occurred.
First, we found that readability of abstracts correlates with the number of co-authors, but this failed to fully account for the trend through time. Second, we showed that there is an increase in general scientific jargon over years. These general science jargon words should be interpreted as words which scientists frequently use in scientific texts, and not as subject specific jargon. This finding is indicative of a progressively increasing in-group scientific language 'science-ese'.
An alternative explanation for the main finding is that the cumulative growth of scientific knowledge makes an increasingly complex language necessary. This cannot be directly tested, but if this were to fully explain the trend, we would expect a greater diversity of vocabulary as science grows more specialized.
While accounting for the original finding of the increase in difficult words and of syllable count, this would not explain the increase of general scientific jargon words e. Thus, this possible explanation cannot fully account for our findings. Lower readability implies less accessibility, particularly for non-specialists, such as journalists, policy-makers and the wider public. Scientific journalism offers a key role in communicating science to the wider public Bubela et al.
Considering this, decreasing readability cannot be a positive development for efforts to accurately communicate science to non-specialists. Further, amidst concerns that modern societies are becoming less stringent with actual truths, replaced with true-sounding 'post-facts' Manjoo, ; Nordenstedt and Rosling, science should be advancing our most accurate knowledge.
One way to achieve this is for science to maximize its accessibility to non-specialists. Lower readability is also a problem for specialists Hartley, ; Hartley and Benjamin, ; Hartley, While science is complex, and some jargon is unavoidable Knight, , this does not justify the continuing trend that we have shown. It is also worth considering the importance of comprehensibility of scientific texts in light of the recent controversy regarding the reproducibility of science Prinz et al.
Reproducibility requires that findings can be verified independently. To achieve this, reporting of methods and results must be sufficiently understandable. Readability formulas are not without their limitations. For example, readability can be affected by text size, line spacing, the use of headers, as well as by the use of visual aids such as tables or graphs, none of which are captured by readability formulas Hartley, ; Badarudeen and Sabharwal, Many semantic properties of texts are overlooked, including the complexity of ideas, the rhetorical structure and the overall coherence of the text Bruce et al.
Changing a text solely to improve readability scores does not automatically make a text more understandable Duffy and Kabance, ; Redish, Despite the limitations of readability formulas, our study shows that recent scientific texts are, on average, less readable than older scientific texts. This trend was not specific to any one field, even though the size of this association varied across fields.
Further research should explore possible reasons for these differences, as it may give clues on how to improve readability. For example, the adoption of structured abstracts which are known to assist readability Hartley and Benjamin, ; Hartley, might lead to a less steep decline for some fields. What more can be done to reverse this trend? The emerging field of science communication deals with ways science can effectively communicate ideas to a wider audience Treise and Weigold, ; Nielsen, ; Fischhoff, One suggestion from this field is to create accessible 'lay summaries', which have been implemented by some journals Kuehne and Olden, Others have noted that scientists are increasing their direct communication with the general public through social media Peters et al.
However, while these two suggestions may increase accessibility of scientific results, neither will reverse the readability trend of scientific texts. Another proposal is to make scientific communication a necessary part of undergraduate and graduate education Brownell et al.
Scientists themselves can estimate their own readability in most word processing software. Further, while some journals aim for high readability, perhaps a more thorough review of article readability should be carried out by journals in the review process. Such an 'r-index' could be considered an asset for those scientists who emphasize clarity in their writing. We aimed to obtain journals from which articles are highly cited from a representative selection of the biomedical and life sciences, as well as from journals which cover all fields of science, which were indexed on PubMed.
There should be more than 15 years between the years of the first five and most recent five PubMed entries. The impact factor of the journal should not be below 1 according to the Thomson Reuters Journal Citation Report. The number of selected journals should provide as equal representation as possible of subfields within the broader research fields.
From each of 11 of the fields, the 12 most highly cited journals were selected. The final field Multidisciplinary only contained six journals, as no more journals could be identified which met all inclusion criteria. Some journals exist in multiple fields, thus the number of journals is below the possible maximum of journals. See Supplementary file 1 for the journals and their field mappings.
The later download date was to correct for originally having only included 11 journals in one of the fields when the data was first downloaded. The text of the abstract, journal name, title of article, PubMed IDs and publication year were extracted. Throughout the article, we only used data up to and including the year Abstracts downloaded from PubMed were preprocessed so that the words and syllables could be counted.
TreeTagger version 3. Scientific texts contain numerous phrasings which TreeTagger did not parse adequately. We did three rounds of quality control where at least preprocessed articles, sampled at random, were compared with their original texts. After identifying irregularities with the TreeTagger performance, regular expression heuristics were created to prepare the abstracts prior to using the TreeTagger algorithm. After the three rounds of quality control, the stripped abstracts contained only words with at least one syllable and periods to end sentences.
Sentences containing only one word were ignored. The heuristic rules after quality control rounds included: removing all abbreviations, adding spaces after periods when missing, adding a final period at the end of the abstract when missing, removing numbers that ended sentences, identifying sentences that end with 'etc.
Examples of texts before and after preprocessing are presented in Supplementary file 3. We confirmed that the observed trends were not induced by the preprocessing steps by running the readability analysis presented in Figure 1D,E using the raw data Figure 2—figure supplement 1.
These measures use different language metrics: syllable count, sentence count, word count and percentage of difficult words. Two different readability metrics were chosen to ensure that the results were not induced by a single method.
FRE was chosen due to its popularity and consistency with other readability metrics Didegah and Thelwall, , and because it has previously been applied to trends over time Lim, ; Danielson et al. NDC was chosen since it is both well established and compares well with more recent methods for analyzing readability Benjamin, Counting the syllables of a word was performed in a three step fashion. First, the word was required to have a vowel or a 'y' in it. Second, the word was queried against a dictionary that contained specified syllable counts using the natural language toolkit NLTK version 3.
If there were multiple possible syllable counts for a given word, the longer alternative was chosen. Third, if the word was not in the dictionary, the number of vowels excluding diphthongs was counted. If a word ended in a 'y', this was counted as an additional syllable in this third step.
Word count was calculated by counting all the words in the abstract that had at least one syllable. The number of sentences was calculated by counting the number of periods in the preprocessed abstracts. The percentage of difficult words originated from Chall and Dale, , defined as words which do not belong to a list of common words. This list excludes some words from the original NDC common word list such as abbreviations, e.
FRE uses both the average number of syllables per word and the average number of words per sentence to estimate the reading level. NDC scores are calculated by using the percentage of difficult words and the average sentence length of abstracts. While the NDC was originally calculated on words due to computational limitations, we used the entire text.
We have used two well-established readability formulas in our analysis. The application of readability formulas has previously been questioned Duffy and Kabance, ; Redish and Selzer, ; Zamanian and Heydari, and modern alternatives have been proposed see Benjamin, However, NDC has been shown to perform comparably with these more modern methods Benjamin, Science-specific common words : Words frequently used by scientists which are not part of the NDC common word list.
This contains units of measurement e. General science jargon : A subset of science-specific common words. These are non-subject-specific words that are frequently used by scientists.
This list contains words with a variety of different linguistic functions e. General science jargon can be considered the basic vocabulary of a ' science-ese'. Science-ese is analogous to legalese , which is the general technical language used by legal professionals. To construct the science-specific common word list, 12, articles were selected to identify words frequently used in the scientific literature.
In order to avoid any recency bias, 2, articles were randomly selected from six different decades starting at the s. From these articles, the frequency of all words was calculated. After excluding words in the NDC common word list, the 2, most frequent words were selected. The number 2, was selected to be the same length as the NDC common word list. To validate that this list is identifying a general scientific terminology, we created a verification list by performing the same steps as above on an additional independent set of 12, articles.
Of the 2, words in the science-specific common word list, The 24, articles used in the derivation and verification of the lists were excluded from all further analysis. The general scientific jargon list contained 2, words.
It was created by manually filtering the science-specific common word list. All four co-authors went through the science-specific common word list and rated each word. The following guidelines were formulated to exclude words from being classed as general science jargon: 1 abbreviations, roman numerals, or units that survived preprocessing e.
Remaining words were classed as possible general science jargon word. The co-authors performed the ratings to identify jargon words independently. However, half way through the ratings there was a meeting to control that the guidelines were being performed in a similar way. In this meeting, the authors discussed examples from their ratings. Due to this, the ratings can not be classed as completely independent.
To compare the readability of full texts and abstracts, we chose six journals from the PubMed Central Open Access Subset for which all full texts of articles were available under a Creative Commons or similar license.
None of them were a part of the original journal list which was used in the main analysis. They were selected as they all cover biomedicine and life sciences and as open access journals, we were legally allowed to bulk-download both abstracts and full texts. However, none of the included journals have existed for a long period of time Supplementary file 4. As such, they cannot be said to represent the same time range covered by the journals used in the main analysis.
Custom scripts were written to extract the full text in the textfiles downloaded from each respective journal. In total, , articles were included in the full text analysis. Both article abstracts and full texts were preprocessed according to the procedure outlined above and readability measures were calculated. We evaluated the relationship between the readability of single abstracts and year of publication separately for FRE and NDC scores. The data can be viewed as hierarchically structured since abstracts belonging to different journals may differ in key aspects.
In addition, journals span over different ranges of years Figure 1C and Supplementary file 1. In order to account for this structure, we performed linear mixed effect modeling using the R-packages lme4 version 1.
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