writingprocess – Learning Aloud

Word processing: Desirable difficulty or opportunities get taken

When it comes to how we use technology, familiarity can limit analysis and exploration. I started thinking about this challenge when encountering the work of an academic who has examined the history of word processing. When I first wrote about word processing in the 1990s, the issues were similar to current topics of whether students should read from paper or screen and whether it was better to take notes on paper or on a laptop. There were comparisons of which method was more productive and efforts to account for the advantages that were identified. It was once similar with word processing. Should students learn to write on paper or using a computer? What were the advantages and disadvantages of each approach? How might the instruction of writing skills with computers be modified to take advantage of the unique capabilities of a digital approach? My thought is that many are now no longer aware of these questions and conclusions and that personal practice and instructional emphases may ignore key findings. This concern seems especially relevant given the new issues raised by the use of AI in writing and learning to write.

I decided to write again about this topic after listening to an interview with Matthew Kirschenbaum on the “This Week in Tech” network’s “Intelligent Machines” podcast focused on Kirschenbaum’s recent focus on the role of AI in writing and learning to write. The podcast guest was a member of the Modern Languages Association (MLA) panel, generating what are likely to become several influential position papers on learning to write and AI. This interview, which makes up maybe the first half hour of the podcast, is worth the attention of any educator trying to make sense of how AI will impact schools and universities. As part of the brief introduction of the podcast guest, Krischenbaum it was noted that the guest had recently written “Trach Changes: Literary History of Word Processing”.As I suggested, word processing had been a personal interest so I did purchase and read the book.

It wasn’t that the book wasn’t well written, but I did struggle to get through it. The podcast focus resulted in my misunderstanding of the topic of the book. The history of the transition from writing on paper and typewriter was of some interest, because I lived through that transition and the mention of technology hardware and software and the required skills involved in writing with a computer brought back plenty of memories. I was less interested in which noted author had made his or her transition from a notepad or typewriter to a word processor during their career. Concerns of the reading community related to how technology might influence literature likely offers similar insights into what some think about AI. A better example might be how Bob Dylan’s fans reacted when he switched from acoustic to electric guitar. What I had falsely anticipated was that the author would examine how digital storage and revision changed writing and the teaching of writing.

I imagined I would encounter an analysis of changes in personal revision, educator feedback and learner revision, peer revision, and possibly even AI as a sounding board for a writer’s efforts. These are the topics in what I see as the evolution of writing and writing education. I decided to generate a post that would offer my own thoughts about the role of word processing in the writing process. The podcast and the book on word processing are still worth your time.

Will digital tools change our writing?

I assume you complete many of the writing tasks you take on using a word processing application. Do you do this because you assume this approach makes you more efficient or do you assume this approach makes you a better writer? Maybe you have never even thought about these questions. However, when functioning as a teacher and asking your students to engage in activities in a particular way, it may be helpful to consider why the approach you expect students to use will be productive. Often, to realize the full potential of an activity, the details matter and some insight into why an approach is supposed to be productive may be helpful in understanding which details to track and emphasize. The following comments summarize some ideas about the value of word processing and of learning to write using word processing applications.

In learning, as in other areas of life, you seldom get something for nothing. Still, a logical case has been proposed for how simply working with word processing for an extended period may improve writing skills and performance. Perkins (1985) calls this the “opportunities get taken” hypothesis. The proposal works like this. Writing by hand on paper has a number of built-in limitations. Generating text this way is slow, and modifying what has been written comes at a substantial price. To produce a second or third draft requires the writer to spend a good deal of time reproducing text that was fine the first time, just to change a few things that might sound better if modified. Word processing, on the other hand, allows writers to revise at minimal cost. They can pursue an idea to see where it takes them and worry about fixing syntax and spelling later. Reworking documents from the level of fixing misspelled words to reordering the arguments in the entire presentation can be accomplished without crumpling up what has just been painstakingly written and starting over.

With word processing, writers can take risks and push their skills without worrying that they may be wasting their time. The capacity to save and load text from some form of storage makes it possible to revise earlier drafts with minimal effort. Writers can set aside what they have written to gain new perspectives, show friends a draft and ask for advice, or discuss an idea with the teacher after class, and use these experiences to improve what they wrote yesterday or last week. What we have described here are opportunities—opportunities to produce a better paper for tomorrow’s class and, over time, opportunities to learn to communicate more effectively.

Do writers take the opportunities provided by word processing programs and produce better products? The research evaluating the benefits of word processing (MacArthur, 2006; Wollscheid, Sjaastad, & Tømte, 2016) is not easy to interpret. Much seems to depend on the experience of the writer as a writer and familiarity with word processing, and on what is meant by a “better” product. If the questions refer to younger students, it also seems to depend on the instructional strategies to which the students have been exposed. It does appear that access to word processing is more beneficial for older learners and some even interpret this difference as having a neurological basis (Wollscheid, Sjaastad, & Tømte, 2016). General summaries of the research literature (e.g., MacArthur, 2006) seem to indicate that students make more revisions, write longer documents, and produce documents containing fewer errors when word processing. However, the spelling, syntactical, and grammatical errors that students tend to address and the revision activities necessary to correct them are considered less important by many interested in effective writing than changes improving document content or document organization. The natural tendency of most writers appears to be to address surface-level features.

Writers appear to bring their writing goals and habits to writing with the support of technology. Beginning writers and perhaps writers at many stages of maturity may not have the orientation or capabilities to use the full potential of word processing, and their classroom instruction may also emphasize the correction of more obvious surface errors. Thus, there are typically improvements in the products generated when working with word processing tools, but the areas in which younger writers seem to improve are not necessarily the most important ones

Many of the potential educational advantages of word processing appear only as students acquire considerable experience writing with the aid of technology and some question whether using a keyboard is better than a pencil for young writers (Wollscheid, Sjaastad, & Tømte, 2016). Perkins’s (1985) argument that writing with word processing programs will improve writing skills because word processing allows students to experiment with their writing makes sense only in situations in which students have written a great deal and experimented with expressing themselves in different ways. The fact that most research evaluating the benefits of word processing has examined performance over a short period of time, with students having limited word processing experience, thus represents a poor test of the potential of word processing (Owston, Murphy, & Wideman, 1992). Research based on a three-year study following elementary students as they learned to write with and without access to word processing opportunities has demonstrated a significant advantage for students with ready access to technology (Owston & Wideman, 1997). A recent study (Yamaç, et al., 2020) examining the benefits of consistent writing on laptops found a similar advantage in contrast to paper and pencil writing tasks for early elementary learners. These researchers point to social media activities such as blogs and multimedia writing with tablets as expanding the writing opportunities available in classrooms.

The National Assessment of Educational Progress (NAEP) demonstrates that in the U.S. greater experience writing with technology is predictive of schools with more proficient writers (Tate, Warschauer & Abedi, 2016). Studies such as this are still controversial as it is difficult to parse out other variables such as the income levels of the majority of students in different schools that may influence both access to technology and writing proficiency. Overall, the role of word processing in developing writing skills depends on the goals of the teacher and individual students, the social context provided for writing, and the amount of writing that students do with the assistance of word processing.

Summary

Many of the posts I write concern the cognitive processes involved in learning, thinking, and academic behavior. Often, I focus on how these processes are impacted for good or bad by involving technology. We seem to be past the point at which educators question writing on a computer, but the distinction I raised between opportunities get taken and desirable difficulty have yet to be resolved with writing. This is clearly the case when educators debate the role AI should play. My suggestions related to the opportunities get taken hypothesis should also be approached would even be that we examine whether the opportunities (often called affordances) of revision are actually employed. Do students get useful feedback from which they might learn to improve what they have written? Despite the likely benefit of revision, do students quantitatively do much revision? Perhaps like other ideals (tutoring, personalizing learning) that are impractical for one reason or another (e.g., cost, teacher time), AI might find a productive role in guiding revision experiences.

References:

MacArthur, C.A. (2006). The effects of new technologies on writing and writing processes. In C.A. MacArthur, S. Graham, & j. Fitzgerald (Eds.) Handbook of Writing Research, pps. 248-262. New York: Guilford.

Owston, R., Murphy, S., & Wideman, H. (1992). The effects of word processing on students’ writing quality and revision strategies. Research in the Teaching of English, 26 (3), 249–276.

Owston, R., & Wideman, H. (1997). Word processors and children’s writing in a high-computer-access setting. Journal of Research on Computing in Education, 30 (2), 202–220.

Perkins, D. (1985). The fingertip effect: How information-processing technology shapes thinking. Educational Researcher, 14, 11–17.

Tate, T. P., Warschauer, M., & Abedi, J. (2016). The effects of prior computer use on computer-based writing: the 2011 NAEP writing assessment. Computers & Education, 101, 115-131.

Wollscheid, S., Sjaastad, J., & Tømte, C. (2016). The impact of digital devices vs. Pen (cil) and paper on primary school students’ writing skills–A research review. Computers & Education, 95, 19-35.

Yamaç, A., Öztürk, E., & Mutlu, N. (2020). Effect of digital writing instruction with tablets on primary school students’ writing performance and writing knowledge. Computers & Education, 157, 1-19.

AI, Tech Tools, and the Writing Process

Student access to AI has created a situation in which educators must consider when AI should and should not be used. I think about this question by considering the difference between what skill or skills are the focus of instruction and whether AI will replace a skill to improve the efficiency of the writing task or will support a specific skill in some way. It may also be useful to differentiate learning to write from writing to learn. My assumption is that unless specific skills are used by the learner those skills will not be improved. Hence when AI is simply used to complete an assignment a learner learns little about writing, but may learn something about using AI.

Writing Process Model

The writing process model (Flower & Hayes, 1981) is widely accepted as a way to describe the various component skills that combine to enable effective writing. This model has been used to guide both writing researchers and the development of instructional tactics. For researchers, the model is often used as a way to identify and evaluate the impact of the individual processes on the quality of the final project. For example, better writers appear to spend more time planning (e.g., Bereiter & Scardamalia, 1987). For educators and instructional designers, understanding the multiple processes that contribute to effective writing and how these processes interact is useful in focusing instruction.

Here, the writing process model will be used primarily to identify the subskills to be developed as part of learning to write and writing to learn and I will offer my own brief description of this model. It is worth noting that other than composing and rewriting products, other uses of technology to improve writing and increase the frequency of writing experiences seldom receive a lot of attention (Gillespie, Graham, Kiuhara & Hebert, 2014).

The model

The model identifies three general components a) planning, b) translation, and c) reviewing.

Planning involves subskills that include setting a goal for the project, gathering information related to this goal which we will describe as research, and organizing this information so the product generated will make sense. The goal may be self-determined or the result of an assignment.

Research may involve remembering what the author knows about a topic or acquiring new information. Research should also include the identification of the characteristics of the audience. What do they already know? How should I explain things so that they will understand? Finally, the process of organization involves establishing a sequence of ideas in memory or externally to represent the intended flow of logic or ideas.

What many of us probably think of as writing is what Flower and Hayes describe as translation. Translation is the process of getting our ideas from the mind to the screen and this externalization process is typically expected to conform to conventions of expression such as spelling and grammar.

Finally, authors read what they have written and make adjustments. This review may occur at the end of a project or at the end of a sentence. In practice, authors may also call on others to offer advice rather than relying on their own review.

One additional aspect of the model that must not be overlooked is the iterative nature of writing. This is depicted in the figure presenting the model by the use of arrows. We may be tempted, even after initial examination of this model, to see writing as a mostly linear process – we think a bit and jot down a few ideas, we use these ideas to craft a draft, and we edit this draft to address grammatical problems. However, the path to a quality finished product is often more circuitous. We do more than make adjustments in spelling and grammar. As we translate our initial ideas, we may discover that we are vague on some points we thought we understood and need to do more research. We may decide that a different organizational scheme makes more sense. This reality interpreted using our tool metaphor would suggest that within a given project we seldom can be certain we have finished the use of a given tool and the opportunity to move back and forth among tools is quite valuable.

Tech tools and writing

Before I get to my focus on AI tools, it might be helpful to note that technology tools used to facilitate writing subprocesses have existed for some time. For example, spelling and grammar checkers, outline and concept mapping, note-taking and note-storage, citation managers, online writing environments allowing collaboration and commenting, and probably many other tools that improve the efficiency and effectiveness of writing and learning to write. Even the use of a computer allows advantages such as storage of digital content in a form that can easily be modified rather than the challenge of making improvements to content stored on paper. The digital alternative to paper changes how we go about the writing process. I have written about technology for maybe 20 years and one of the bextbooks offered the type of analysis I am offering here not about AI tools, but about the advantages of writing on a computer and using various digital tools.

A tool can substitute for a human process or a tool can supplement or augment a human process. This distinction is important when it comes to writing to learn and learning to write. When the process is what is to be learned, this substitution is likely to be detrimental as it allows a learner to skip needed practice. In contrast, augmentation often allows the opposite as a busy work activity or some incapability is taken care of allowing more important skills to become the focus.

Here are the types of tools I see as supporting individual writing processes.

Planning – Organization and Research

Prewriting involves developing a plan for what you want to get down on paper (or screen in this case). A writer goes about these two subprocesses in different ways. You can think or learn about a topic (research) and then organize these ideas in some way to present. Or, you can generate a structure of your ideas (organize) and then research the topics to come up with the specifics to be included in a presentation. Again, these are likely iterative processes no matter which subskill goes first.

One thing AI does very well is to propose an outline if you are able to generate a prompt describing your goals. You could then simply ask the AI service to generate something based on this outline, but this would defeat the entire purpose of learning about the topic by doing the research to translate the outline into a product or developing writing skills by expanding the outline into a narrative yourself.

Since I am writing about how AI might perform some of the subskills identified by the writing process model, I asked ChatGPT to create an outline using the following prompt.

“Write an outline for ways in which ai can be used in writing. Base this outline on the writing subprocesses of the writing process model and include examples of AI services for the recommended activity for each outline entry.”

The following shows part of the outline ChatGPT generated. I tend to trust ChatGPT when it comes to well established content and I found the outline although a little different from the graphic I provided above to be quite credible and to offer reasonable suggestions. As a guide for writing on the topic I described, it would work well.

I had read that AI services could generate concept maps which would offer a somewhat different way to identify topics that might be included in a written product. I tried this several times using a variety of prompts with ChatGPT’s DALLE. The service did generate a concept map, but despite making several follow-up requests which ChatGPT acknowledged, I could not get the map to contain intelligible concept labels. Not helpful.

Translation

Tools for improving the translation process have existed in some form for a long time. The newest versions are quite sophisticated in providing feedback beyond basic spelling and grammatical errors. I write in Google docs and make use of the Grammarly extension.

I should note that Grammarly is adding AI features that will generate text. Within the perspective I am taking here I have some concerns about these additions. Since I am suggesting that writing subskills can be replaced or supported, student access to Grammarly could allow writing subskills the educator was intending students to perform themselves to be performed to some degree by the AI.

If you have not tried Grammarly, the tool identifies different types of modifications the tool proposes different modifications the writer might consider changing (spelling, missing or incorrect punctuation, alternate wording, etc.) and will make these modifications if the writer accepts the suggestion. The different types of recommendations are color-coded (see following image).

Revision

I am differentiating changes made while translating (editing) from changes made after translating (revision). Minor changes such as spelling and grammar would seem more frequently fixed as edits by this distinction and major modifications made (addition of examples, restructuring of sections, deletion of sections, etc.) while revising. Obviously, this is a simplistic differentiation and both types of changes occur during both stages).

I don’t know if I can confidently recommend a role for AI for this stage. Pre-AI, one might recommend that a writer share their work with a colleague and ask for suggestions. The AI version of Grammarly seems to be moving toward such capabilities. Already, a writer can ask AI to do things like shorten a document or generate a different version of a document. I might explore such capabilities out of curiosity and perhaps to see how modifications differ from my original creations, but for work that is to be submitted for evaluation of writing skill would that be something an educator would recommend?

I have also asked an AI tool to provide an outline, identify main ideas or generate a summary of a document I have written just to see what it generates. Does the response to one of these requests surprise me in some way? Sometimes. I might add headings and subheadings to identify a structure I thought was not as obvious as I had thought.

Conclusion:

My general point in this post was that questions of whether learners can use AI tools when assigned writing tasks should be considered in a more complex way. Rather than the answer being yes or no, I am recommending that learning to write and writing to learn are based on subprocesses and the AI tool question should be considered in response to a consideration of whether the learner was expected to be developing proficiency in executing a subprocess. In addition, it might be important to suggest that learning how to use AI tools could be a secondary goal.

Subprocess here were identified based on the Writing Process Model and a couple of suggestions were provided to illustrate what I mean by using a tool to drastically reduce the demands of one of the subprocesses. There are plenty of tools out there not discussed and my intention was to use these examples to get you thinking about this way of developing writing skills.

References:

Bereiter, C., & Scardamalia, M. (1987). An attainable version of high literacy: Approaches to teaching higher-order skills in reading and writing. Curriculum inquiry, 17(1), 9-30.

Flower, L., & Hayes, J. R. (1981). A cognitive process theory of writing. College composition and communication, 32(4), 365-387.

Gillespie, A., Graham, S., Kiuhara, S., & Hebert, M. (2014). High school teachers use of writing to support students’ learning: A national survey. Reading and Writing, 27, 1043-1072.

Obsidian/Smart Connections Workflow

I use Obsidian plus the plugin Smart Connections to inform my blog writing activities. I write for educational practitioners and academics so I try to carefully base my content on sources that I have read and in many cases intend to cite in the content I generate. With this goal, Obsidian represents an archive I have developed over several years to store and organize notes from hundreds of books, journal articles, and websites. I explore my collection in different ways sometimes seeking notes on a specific article I want to emphasize and sometimes exploring to locate what I have read that is relevant to a topic that I might want to include but perhaps do not recall at the time.

In some cases, I want to use an AI tool to support my writing. I seldom use AI to actually generate the final version of content I post, but I may explore the possible organization of material for something I want to write or I might use an AI tool to generate an example of how I might explain something based on the notes I have made available to the AI tool.

The combination of Obsidian augmented by the Smart Connections plugin allows me to implement a workflow I have found useful and efficient. I have several specific expectations of this system:

I have already read the source material and taken some notes or generated some highlights now stored in Obsidian. I want to write based on this content.
I may not recall relevant sources I have stored in Obsidian because of the passage of time and the accumulation of a large amount of material. I want the AI system to understand my goals and locate relevant content.
I want the AI system to identify specific sources from the content I have reviewed rather than the large body used to train the LLM. I want the system to identify the specific source(s) from this material associated with specific suggestions so that I am aware of the source and can cite a source if necessary.
When a specific source has been identified I want to be able to go directly to the original document and the location within that document that is the location for the note or highlight that prompted the inclusion in the AI content so that I can reread the context for that note or highlight.

Obsidian with the Smart Connections plugin does these things and is to some extent unique because all of the material (the original content) is stored locally (actually within iCloud which functions as an external harddrive) allowing the maintenance of functioning links between the output from Smart Connections, the notes/highlights stored in Obsidian, and the original documents (pdfs of journal articles, Kindle books, web pages).

I do not know for certain that the Obsidian-based approach I describe is the only way to take the approach I take. I am guessing my approach works in part because I am not relying on an online service and online storage. I also use Mem.ai because it allows me to focus on my own content, but linking back to source documents does not work with this service. Mem.ai does include the AI capabilities as part of the subscription fee, but I don’t know when this might be an advantage. The Smart Connections plugin does require the use of an OpenAI API (ChatGPT) and there is a fee for this access.

Example:

Here is an example of what working with the Obsidian/Smart Connections setup is like. I am working on a commentary on the advantages and disadvantages of K12 students having access to AI in learning to write and writing to learn. I propose that writing involves multiple subprocesses and it is important to consider how AI might relate to each of these subprocesses. My basis for the list of subprocesses is based on the classic Flower and Hayes Writing Process Model. I had written a description of the Writing Process Model for a book I wrote and this section of content was stored within Obsidian as well as notes from multiple sources on AI advantages and disadvantages in the development of writing skills. I have not read a combination of the writing process model with ideas about the advantages and disadvantages of AI so this is the basis for what I think is an original contribution.

The following is a screenshot of Obsidian. The Smart Connection appears as a panel on the right side of the display. The left-hand panel provides a hierarchical organization of note titles and the middle panel provides access to an active note or a blank space for writing a new note.

In the bottom textbox of the Smart Connections panel, I have entered the following prompt:

Using my notes, how might AI capabilities be used to improve writer functioning in the different processes identified by the writing process model. When using information from a specific note in your response, include a link to that note.

Aside from the focus of the output, two other inclusions are important. First, there is the request to “use my notes”. This addition is recommended to ensure a RAG (retrieval augmented generation) approach. In other words, it asks the AI service use my notes rather than the general knowledge of the AI system as the basis for the output. The second supplemental inclusion is the request to include a link to that note which is intended to do just what it says – add links I can use to to see where ideas in the output came from.

The output from Smart Connections is in markdown. I copied this output into a new blank note and the links included are now active.

I purposefully selected a note that initially was part of a web page for this final display. I had originally used a tool that allowed the annotation of web pages and then the exporting of the annotated and highlighted content as a markdown file I added to Obsidian. This file included the link from the note file back to the online source. As you can see, the link from Obsidian brought up the web page and with the assistance of the activated service added as an extension to my browser displays what I had highlighted within this web page. Interesting and useful.

Conclusion:

We all have unique workflows and use digital tools in different ways because of differences in what we are trying to accomplish. What I describe in this post is an approach I have found useful and I have included related comments on why. I hope you find pieces of this you might apply yourself.