Part 3 of 3 – Supercharge teacher effectiveness with learning stories that support a whole-of-student view.
From creating a clear intention to declutter data in Post 1 through to building a framework around data in Post 2, Post 3 describes a platform approach to “how you do it”. Avoid BIG DATA syndrome and building lots of complex charts. Teachers simply want better visibility into data.
When K-12 teachers instinctively access teaching and learning data to maximise their effectiveness, the elephant named DATA has truly LEFT the room!
Having access to and working with data, as a habit, is a primary goal for every school and teacher. Finding the time to retrain as a data analyst or scientist is completely something else.
In 2019 it’s terrible to think that data remains the elephant in the room in most teaching and learning meetings. Everyone knows data is out there, most have seen bits of it but pretty much everyone wishes they could see it all. ‘Data science’ as a practise has claimed an undeserved ownership over data, building a mystique around data collection and presentation. In reality, data scientists only have to train a computer to do some pre set moves. I think the dynamics and variables involved in teaching cognitive humans is far more involved and complicated. This is why teachers need to have access to data stories to support them in their context.
In fact, all of the hype around data science and analytics detracts from what data really gives teachers; a Dynamic Ability To Affect. Access to data must underpin a Teaching As Usual framework, devoid of jargon, latency and complexity.
For now it’s all about keeping our eyes on the real prize. The ultimate value of data in K-12 schools comes from the learning stories teachers use to affect and personalise learning. The easier it is for teachers to find stories in data, the more effective teachers are and the stronger the use of data becomes.
Helping teachers to see into teaching and learning stories is the ‘now’ conversation schools want to have. Getting teachers connected with their hypotheses and instincts is a priority. Personally I think 99% of teachers know there is data orbiting them with differing dimensions of volume, velocity and variety. The core problem is that teachers don’t have a simple and time efficient way of accessing data and seeing the story it tells. What if this could change?
In Post 1 of this series, I started a conversation about the need to declutter our minds about data and only work with data that brings joy to teachers. In Post 2, I introduced the idea of six data domains, into which all K-12 teaching and learning data falls. As with all things in life, the devil lives in the detail. With Multiple Data Sources and Elements within each Source, the core of all data challenges and usefulness comes down to a single word, Alignment.
Think back to the Rubik’s cube metaphor. Alignment describes the process by which the cube is solved. If data alignment is not robust, the momentum around it and support for its use ends quickly.
I now want to take a look at more detailed data alignment challenges schools face as they execute on ‘Delivering the whole of student view’. The guiding presumption from this point is that Schools want to organise their data Domains and Sources to deliver a ‘whole of student view’ across all teaching and student learning data.
You must develop an efficacy balance between learning data and teacher engagement, all in the context of learner stories.
This post is not meant to get technical and make an over-talked topic even harder. We all need to focus on strategies that declutter the way we think about data and in so doing improve visibility into it. There are two parts to working with data and both parts need to work together.
- If data is not well curated, the potential stories and usefulness will reduce.
- If the data exists and the stories can’t be seen, you miss out on the whole point of looking into data; to inform teaching.
Alas, the yin and yang is the collection of and visibility into data – described in one word again, alignment. Following along the theme and challenge of ‘alignment’ there are two steps to take.
Step 1. Create DATA Alignment
1.1 Align all learning data to the atomic level of a Student.
10 seconds: Make sure that all of your teaching and learning systems support a single unique Student reference. If you lose control of alignment of data to students you are disconnected with learning.
Bigger Discussion: Individual students reside at the atomic core of all K-12 data. The seemingly simple task of aligning students to data across data Domains and Sources still remains one of the biggest and most time consuming challenges schools face. The more applications schools use, the more student reference fractures appear. Ideally, every student should have data that follows them by way of alignment to classes, groups and cohorts as well individually.
What to do: Mandate a standard Data Alignment Policy. Every application must integrate a unique student ID or school student email address as the key field against which all data is recorded. All learning data must also be ‘extractable’ and explainable from the various applications used. If your applications don’t support your student data alignment policy, find new applications. Simple. This includes diagnostic testing applications. This sounds harsh but face it. If you can’t see into the data, why bother collecting it?
1.2 Alignment of Data Sources – consider 3 words – Volume – Velocity – Variety
10 seconds : Data comes from many systems, in a variety of formats, in large volumes, at different cycles and velocities. Understanding the Volume, Velocity and Variety of your Data Sources, and the Data Elements contained within, is really important for aligning data with your teachers.
Bigger Discussion: Dealing with data Volume, Velocity and Variety is a challenge. Variety describes the richness of data in each Data Source. Volume and Velocity describe the quantity and pace at which data is collected in your school. When you have lots of varied data that contains critical storylines, you need to take a breath and think about how these data sources can align to what you are looking for.
NAPLAN or SAT’s for example have hundreds of available Data Elements per student across five domains of testing – this represents data Volume and Variety at its best. Luckily NAPLAN and SAT’s are only once a year. There are also very simple Data Sources like Attendance data where the Variety and Volume rates are low but the Velocity at which teachers need to know is instant.
What to do: With varied data surrounding schools, the best option is to understand all of your key data supply lines, their similarity, the volume and variety of information they carry and the velocity at which the supply lines of data move. Work out what you really need to know! Quick moving data builds learning stories near to real-time. Lumpy legacy data is just that, Be brutal with data, consider only what can add value to a story for a teacher in class. If you get that right, most other data demands fall into line.
1.3. Align Metadata across Data Sources within Data Domains
10 seconds : All data recorded across K-12 schools share common ‘metadata’ attributes like Year, Class, Subject and Term. Sometimes these attributes are not obvious, contained or captured in base Data Sources. That translated means, you must make it easy for teachers to find consistent comparison data between different Data Sources across Domains and the way you do that is via metadata.
Remember the 6 Data Domains I discussed in Blog 2? Every Data Source will fit logically into one of these Data Domains. From within these Domains stories are built.
Within Data Domains, distinct Data Sources will have differing scales, ranges of scores and skills tested. Try to align as many common metadata points as possible. For example, snapshots like NAPLAN, SAT , PAT and ALLWELL are all Data Sources in the Diagnostic Domain sharing common metadata like Year, Cohort, Test Name, Subject, Date, Band (possibly). This data is fundamental to building consistent inquiry models across Data Sources.
Then there are less structured Data Sources with more data diversity, much of it being text based commentary. These Sources are more transactional in nature, are more variable across Year groups and come from different source systems with differing volumes and velocities. Pastoral and Formative Assessment platforms, (as an example), all add daily data volumes across many students at multiple year levels in a school, especially year 9! Every time data is recorded about a student in a different year group, for example, data changes.
What to do: Aligning Data Sources to Data Domains and then defining Data Elements is the task. It’s a great process to get you in touch with your inner data and actually best done by non-data people. The Elements like Score, Grade, Percentage, Curriculum link, Skill and Sub Skill should be aligned between different Data Sources. Some Data Sources also hold unique Data Elements that cannot be overlooked and need to be blended in.
Step 2. Create learning stories with a mantra of connecting with teachers rather than charts
The biggest challenge remains. How to simply and quickly connect audience-centric storytelling to leaders, teachers and students? The intention is as simple as the Marie Kondo mantra of life, happiness and meaningful surrounds. Declutter our lives, time, thinking (and family…yikes) by removing the ‘stuff’ that really doesn’t make a positive difference.
For schools, that means move incomplete data and complex charts out of the way. Focus on the connection you are trying to make with your leaders, teachers and students. Complex presentation layers do not correlate with greater connection to your audience. Teachers find no joy in data if there is no teaching and learning context.
Brainstorming with Teachers and Leaders always helps declutter what really matters to them. We spent time in a ‘Kondo’ style session with some great teachers. The floodgates opened and in minutes we had 10 points. “Can we….”
- See all important data in one place without having to interpret a chart?
- Read the highlights of the data summarised in simple sentences?
- Simply link into my classes and students?
- Bring Pastoral and academic data into view for any student or cohort?
- Cherry pick data from different Data Sources and view them side by side?
- Set alerts to monitor data events that draw my eye to look more into?
- Quickly identify interventions and differentiation?
- Follow hunches into comparisons across results from different data?
- See a whole-of-student view across all sources for handover year on year, term on term?
- Engage Parents and Students with the same full learning story.
Remember that running right alongside this wish list, there are six levels of audiences (School, Cohort, Class, Teacher, Students and Parents) that you have to plan for. My advice is to use the same structure and data aggregation logic across all layers of audiences. Rubik cube logic, again!
For a teacher looking across the cube, the cube represents their class or Cohort. For a Student, the cube represents their individual learning story across all Domains for all years.
All of a sudden, diverse, multi-data source inquiries that end up looking like these, are all by your design.
What do you do with all of this data? How do you get stories moving?
10 seconds : Focus on the challenge of getting stories and data into the hands of your teachers, simply. Teaching and Learning is a team sport played daily, not a science or IT project. Consensus now suggests there is limited impact in giving teachers multiple charts, many more system touch points and dumps of learning analytics. I think the mission to be accomplished is to actually deliver learner insights from data, rather than place another data fed elephant in the room.
What to do: With a robust structure around data – where are you going to keep it and how will you mobilise it? K-12 schools have LOADS of data. Right about now is when technical people start talking up terms like Data Lake, BI tools and data Warehouse, all the way back to good old Excel.
You have three main options really. All are directly related to bravery and investment limits along with getting an extensible result that works for you.
- You can call in Data ‘analytics’ experts to define all your data. Keep in mind that you will probably be teaching them about your data Sources and Elements! Usually this starts the iterative process of asking how you would like your charts to look to solve hypotheses for leaders, teachers, students and parents. Understand the all up hours and license costs of BI tools before you start. Bravery Level – HIGH . Financial Investment needed – HIGH, Result – ALL UP TO YOU
- You can start configuring a Data Lake and all of the engineering that goes with that yourself. Many times the data analytics people from Option 1 will suggest or do something like this. Ask yourself the question “Are you a school or an IT bespoke shop?” Bravery Level – HIGHEST. Financial Investment needed – HIGH, Result – ALL UP TO YOU
- You can work with a supplier that has a turnkey approach to Data Sources and Data Elements in K-12 schools. Repeatable data integration done for a school rather than by a school will produce a result that can be used by teachers instantly. Literatu Learning Ledger is a turnkey platform for K-12 Schools. Remember there is no unique IP in common data sources. IP comes from doing something of value with the data. Bravery Level – VERY CONTROLLABLE, Financial Impact – LOW, Result INSTANT
There are 10 important questions to ask yourself and others, as you look to build data stories from the data you have. What you have to maintain is Alignment and currency of data. Don’t’ let data stories go off with a lack of refresh, build and iterate them across the school one by one. Look for feedback and always try to answer the ten questions teachers asked us, above.
In terms of picking a partner to help unfold data in your school, you have to think about these 10 core questions.
- If we have 10 Data Sources today, what happens when I blend another one? How do we rewrite and blend all the charts and rules?
- Can data loads be automated and standardised so that we don’t have an IT budget blowout? Is there a service that can help do this reliably?
- We don’t really know how teachers will consume the data we have or how we ideally want data to be presented. Can our teachers build dashboard concepts share them and discover what works ourselves? What if we really don’t like charts?
- Can we aggregate Data Elements on demand, from any Data Source ?
- Do teachers have to know how to use data analytics packages to inquire into data? Is there an extra license we need to purchase to do this?
- Can teachers include their spreadsheets as valid Data Sources?
- Can we see data across weeks, terms and years, making handover of Students easier as they move ahead?
- Can we look across Cohort and House level data views?
- Can Students and Parents log into the same system as Teachers?
- Can we develop learning stories in a narrative format – rather than charts?
If you cover off these questions, you will have made a good start. Managing data sources is one thing, building a data culture comes next. What comes first? Show data, talk about what you see, find what you need and repeat. Until you do this, you won’t be able to guide a culture with any supporting layers of inclusion. Last I checked, culture was not formed from policy but rather an unconscious competence and repeatable behaviour.
Show data, talk about what you see, find what you need and repeat. When all of this happens by default, across most of your teachers, you will build a data culture to support teaching and learning decision making for years.
Mark Stanley is CEO and Founder of Literatu.
Literatu Learning Ledger builds a whole-of-student view across multiple data sources for K-12 schools, globally.
Open up learning data so that teachers are free to see
Part two of a three post series looks deeper inside the data closet and possibilities of K-12 schools. Lurking in all corners is data from multiple sources that when unleashed, often compete for attention. All of a sudden BIG DATA syndrome lurches forward into an unsuspecting audience. Keeping data terminology simple is of paramount importance. Post 3 will discuss how to run with the energy you will unlock in this post and deliver a framework for teachers.
I often ask teachers who come along to “data for learning” sessions in schools, “What data really matters to you?, “What data visibility would help you to help your students?” Surprisingly, most teachers don’t lead with “All I need is Power BI and a consultant to develop charts and data analytics”. Resoundingly teachers see their priority as doing the best they can to help their students achieve the absolute best they can. As true as that is, I never really get an answer. “What have you got?” is the most common response. It’s like a standoff; “show us what you have and we will take a look”. It is a reasonable response considering many schools don’t have a system for organising, packing and unpacking data in a way that everyone feels a part of culturally. This unfortunately describes many of the to-and-fro data machinations schools go through when dealing with data.
More times than not, data in schools is rarely imagined as a fountain of collaborative knowledge. Data is thought of as more like secret silos of collective recount and just another thing to manage and transpose. Not all teachers know where to go to access data or indeed what they could ask for. Online systems like OARS and SCOUT, and many others, open a limited window into data that external providers want you to be happy with. These systems have multiple logins and access points to remember and never really bring data back into practical frame of reference for teachers. Data is described and displayed as envisioned by developers. For example, SCOUT can tell you about one student at a time, not the class or cohort. Most times, less than 20% of teachers in a school access these systems. That means 80% of teachers don’t have the time and find access too hard. Not a good run rate for any measure of progress.
So, how do we progress? There is an element of conscious incompetence when it comes to knowing what data there is and what value it holds. Another way of saying this is that many teachers don’t know what they don’t know. Until a ‘Kondo’ declutter event happens both in the physical data closet, and in our thinking, there will be no new windscreen through which teachers can explore a hunch or find a new direction. The current rear vision mirror, useful for lane changing in traffic, will persist. I recommend a simple Kondo-style approach to data organising, starting with a common framework to build momentum.
We can’t advance data conversations without a framework that underpins data. We can’t continually talk about ‘stuff’ nor can we declutter our ‘stuff’ into a containerised system if we don’t have containers. I believe there are six primary data domains in every school. That’s six ways we can declutter data.
- Behavioural – Attendance – Compliance
- Pastoral wellness – Participation
- Academic growth
- Targeted skills development – Formative – reading , writing , maths – and ‘apps’ for that
- Observation – Cognitive capture
The list of Data sources I highlighted in Post 1, all neatly slot into these Domains. In all categorisation challenges, the key phrase to remember is ‘less is more’.
Finally I get to build out my metaphor for Blog 2. De-boned, these domains represent the six faces of a Rubik cube. The Rubik cube represents the complete student learning data story of each school.
When teachers ask , “what teaching and learning data do we have?” the answer is that we have six Data Domains. Imagine each one as a side of a Rubik cube.
NAPLAN, PAT and ALLWELL are great examples of Diagnostic Data Sources. Every Data source logically belongs in one of the six Data Domains and is a row or box on the Diagnostics Domain face of the cube. Each Data Source has a context, year and other surrounding metadata that makes it either unique or simply more of the same thing. Let’s label the Diagnostic domain as Orange in color and each Diagnostic Data Source as a row on the Diagnostic cube face. The rows could be organised by year or more granular learning breakdowns like Type of Test, Reading, Writing, Comprehension (that’s the easy part done by data people).
There it is. We have a base structure into which the initial clutter of diagnostic data Sources are organised. When teachers want Diagnostic information, there is a Domain in which all diagnostic Data Sources live and everyone uses the same terminology. Make sense? Now it’s time to go one more level.
The clutter and detail found in most Data Sources is actually in the Data Elements, the pieces of information and specific content contained inside each Data Source. NAPLAN, for example, is a very rich source of many data elements. From Band to Scaled Score to question correctness, multiple data Elements exist within each data source. Don’t worry about the elements now. Good data storage will offer you choices around these data elements, hopefully in a big menu, tick box format.
Your initial burning question can now can now be expanded confidently, knowing you have the right ‘stuff’ in the right place.
- I am after Diagnostic information ( Domain)
- From NAPLAN 2018 ( Data Source)
- ..and I would like to see Band, Scaled Score and Raw Score for Reading (Data Elements).
Now, all you have to decide is the volume of data you want. Do you want this information for:
- A Student or Students?
- A Class, your Classes? (because you are a teacher with a roster so that would really help)
- A Cohort / House / Year or other aggregation?
How to think about data across Domains and Sources and then run free.
Imagine this new Rubik Super-Cube in your hands. You have data from your six Data Domains in sight. For a student you can see across all Domains of data, as you can for a cohort or class. The Cube metaphor implies that all volumes of data across all six Domains is available at any level of inquiry. Be it, Student, Class, Cohort, Subject, House or Year, the query is the same. The only difference is the amount of information you want to look into.
With this metaphorical magic in your head, you can now frame any question using your general intelligence, something you have and computers don’t.
“Can I see Pastoral data on Personal Development (a row Data Source from Pastoral Domain- Yellow) against the Reading results from NAPLAN 2018 (a row Data Source from Diagnostic Domain- Orange)?” I usually add ‘Please’ but Computers don’t know that word either.
Imagine twisting your Rubik Pastoral – Personal Development Data Source row across the face of the Diagnostic – Reading Data Source. You have your view side by side and the cognitive ability to run with it. As with a real Rubik cube, your twists and turn combinations are up to your imagination.
The only remaining question is what Data Elements you would like to see from each Data Source? This will define the level of detail you want from each Data Source to quench your insatiable thirst for learning growth insights.
Everything improves from a solid and consistent starting framework. These examples are just some simple starting gymnastics that you can do with organised Data Domains and Data Sources.
To recap the main points in this post.
- There are six Domains of data in K-12 Schools.
- In each Domain, there are multiple Data Sources. Start by finding a few but do understand that when a new Data Source arrives, it will fit into a Domain. That’s the Kondo ‘less is more’ way!
- In Each Data Source, there are Data Elements to see. This is Post 3.
The real excitement comes when your school has a platform approach to data, one that allows everyone to confidently explore data up, down and across the cube, the way they want to. I started at the top of this post with and image of the end game.
In my final post, I will talk about how all of these Cube formats with Domain and Sources completely mixed, all make sense. Sometimes the answers you seek come from multiple Domain, Source and Element data pieces. Why not? The structure of the data should support the interest you have.
When you have a framework in motion you can construct any combination of inquiry. The most important feature of any system is the ease at which you can snap back to a fully solved puzzle and start again. This would be like having Marie Kondo come back into the room and declutter all over again. Yes indeed, this is mandatory!
Mark Stanley is CEO and Founder of Literatu. : www.literatu.com
Declutter your data to find real student learning stories
Part one of three posts introduces the idea that for K-12 schools to be successful in using data to grow learning they need to declutter their thinking, and data. Giving teachers a clear line of vision into each student’s learning story is the Kondo goal. Posts 2 and 3 will discuss how to get a Kondo mindset working at your school to progressively build a beautifully organised learning support space.
Marie Kondo’s decluttering philosophy has people everywhere re-thinking how simplicity trumps clutter and stress. This simple idea of people being in control of all of their ‘stuff’ moves to the top of everyone’s to do list as soon as they read Marie’s book or watch her TV series. Decluttering applies to our book collections, Facebook friends, and even our family members. The same simple principle applies to K-12 schools and the data they collect in cluttered, stress-driven systems.
In a nutshell: Schools should decide on and organise the learning data that brings joy to teachers’ lives every day, and release what doesn’t. If there is no alignment to teaching and learning, there is no joy in data for teachers. Don’t clutter an already busy life.
The popularity of Kondo’s approach proves how good it feels to eliminate excess in our lives and get back to what matters. If Marie Kondo came to your school to help, what would your goal be? I would suggest a great ‘Kondo’ goal challenge; to have a learning story for every student, in a single accessible and uncluttered space! The one place Kondo hasn’t tackled, however, might be the most cluttered of all: our minds. Let’s get some clear thinking around how we could approach our first goal.
Step 1 . What level of visibility into a single student learning story would delight teachers right now?
Step 1 is not a trick question. I asked a Learning Director at a large school how many data sources the school collected or had access to, somewhere. In about 20 seconds flat, we had a list.
NAPLAN – for 10 years (across four domains), PAT Early Years, PAT-M, PAT-R, PAT Spelling and Vocab and Science for many years and cohorts, E-WRITE, MYAT, ALLWELL M, R, C, Flourishing, Education Perfect, Mathspace, Mathletics, Maths Online, Probe, PM Benchmark, Soundwaves, Edumate, Observations, Snapshot, Valid Science, PISA and Canvas.
Like every school, there is always lots of data and with that data comes cluttered thinking around what could and should be done with it. All of these data sources can’t have the same importance to a teacher at once. The sheer process of streamlining can unleash a barrage of decisions that don’t really add value to achieving your goal: Should we include all of our data not just some? Why not build a data warehouse? If we have that, we really need this as well! Can we have charts and dashboards across all data? Before you give yourself a chance to declutter, your mind is again full of clutter.
If you focus your thinking around what data makes the biggest immediate impact to teachers, what would that be? In our experience with many schools, we can highlight what we get asked, in the order we get asked. The ultimate goal is always to build a single student view that delivers clarity and relevance to every teacher, reducing their mental clutter of worrying about what they don’t know. Some teachers suffer from real FOMO stress.
- Diagnostic data. There is a lot of good data available from multiple sources. It’s accurate, complete and accessible. This data usually resides in the same closet space, a shared drive full of excel spreadsheets. The clutter, however, is in the data itself. It is hard to shape into a single learning story from multiple formats and scales. NAPLAN, PAT, ALLWELL and PISA offer the most accurate insights in many formats. Decluttering is a simple process when you know how.
- Pastoral data has a high priority for visibility and clarity. This data lives in systems that are often inconsistent in detail and context. Pastoral data comes from legacy SIS attendance, extra curricula and behavioural data, surveys and specialised assessments each providing different angles of view across each student’s plane. Importantly, this data is not prescriptive. Pastoral data ignites the cognitive experience of teachers with a backdrop of insights into potential barriers and opportunities. Again, a clear alignment to each student keeps this relevant and simple.
- Report data. Yes, the classic academic reporting data built twice a year, usually, is still the big apple that seldom falls far from the tree. The importance of this data is truly realised when it can be easily compared to diagnostic and pastoral data. It’s the process of alignment of these sources that brings the most joy.
- Reliable data from other systems. This is typically where clutter and fragmented information re-enters the mix often through multiple Gradebooks. LMS data, formative and specialised application data is usually fragmented and, yes, cluttered with all sorts of ‘stuff’’. The challenge with this data is to work out what part of teacher joy it contributes to, if at all?
Schools suffer at the hands of clutter so much so that SAU (Schooling as usual) is all teachers can do with the information they have. What would happen if Marie Kondo came to talk to you about the clutter in your data closets? Would she focus on decluttering the data closet or the real clutter that may be holding us all back; what we think teachers need to live joyously and FOMO free?
In Part 2 of this series we will talk about how to organise your important data, taking what you need and delighting teachers with simple uncluttered access. What joy!
Mark Stanley is CEO and Founder of Literatu.
Get Insights before you Read?
As a former English teacher, I’ve spent many hours over many weekends putting feedback on students’ writings. As time-consuming as this was, I knew it was important. Among the best things that came from the experience of reading a class set of texts was the shortlist of notes I jotted down when I noticed some common issues or skill gaps. These could range from the correct use of semi-colons to strategies for using transitions between paragraphs or the more subtle arts of drawing inferences from quotations rather than just repeating their main ideas.
As bleary-eyed as I might be, I returned to school enthusiastically, knowing that I had specific ways to help students improve their writing. I never dreamt that someday I would be able to get such insights and examples without reading a single papers. But with Scribo, you can! Let me explain…
Insights and Work Samples > Possibilities for Targeted Teaching
As soon as students have submitted their digital texts (from any sources such as their hard drive, Google Docs, Word or even PDFs), you click on the report button. This sets Scribo into action and it applies over 30 analytics and AI routines across every word, sentence ad paragraph for each text. Imagine how low it would take you to do such a thing. Scribo typically does it in 3-4 minutes for the whole class. There are literally dozens of insights and text samples teachers can use, but here are my favourites so far:
- Quickly see how “on-topic” students are. If many students haven’t addressed the topic deeply or broadly enough, you can have a quick brainstorming session on how to address more aspects of the topic.
- Sometimes the number of paragraphs is significant, one click sorts the class list by paragraph count. A quick look at those with too many or too few paragraphs provides a teachable moment with anonymous sample texts.
- Explore the range of vocabulary used by students and see some of the “fancier” words in the very context of the sentences in which they were used. This is a great assist when students are turning to the Thesaurus and might need help refining their understanding and usage of the words.
- Cohesive words are what Scribo calls conjunctions, connectives and transitions. A very handy “Cohesive Explorer” divides a list of hundreds of cohesives into common and advanced groupings. Common cohesives are the basic connectives, whereas Advanced cohesives connote such advanced ideas as concessions, clarifications and inferencing. Once students have learned the basic structure of body paragraphs in informative or persuasive essays, using the Cohesive Explorer really empowers them to show their more sophisticated thinking by prompting them with possible alternatives.
If you haven’t tried Scribo yet, get in touch. We have a great sandbox site where you can try out all of Scribo’s features with a range of pre-loaded texts.
Our Real Goal
To begin with the obvious and inarguable: we want students to keep getting better at writing. Because our job is to help students to keep getting better at all aspects of their education. If we accept the premise that our goal is to improve student writing, why not explore new approaches that can reduce the burden while increasing effectiveness?
Let Software Do…
My mantra, as a devout English teacher, writer and long-time Ed Tech entity is simple and clear: “Let software do what software can so teachers do what only teachers can.” Can software analyse student writing as well as a trained teacher in writing? Of course not. But everyday we all rely on things that software can do, such as spellcheck our work and facilitate editing. Such functionality is second nature to us. It is also about 30 years old. As quickly as technology has changed in that time, especially in regard to crunching data into profiles, noticing patterns, and comparing disparate bits of data, can’t we imagine that the science of text analysis has evolved? It has. In little steps. Little, because communicating and language are among the most complex things we humans do.
The argument against machine reading of students’ writing is that no computational reading of a text can critique, let alone notice, such things as irony and poetic intent. Nor can it reward a particularly well-turned phrase. When we humans engage in our “labour of love”, scribbling detailed feedback on students’ papers, we are often looking for just such things. Unfortunately, we inevitably confront repetitious and limited word choice, poorly structured sentences and paragraphs that lack integrity. Things that we would hope students addressed in earlier drafts of their work. Drafts?
What Software can, so…
Interestingly, it was also 30 years ago that the Writing Process captured the interests of university researchers, writers and teachers. We noted that “expert writers” did things that “novices” did not, such as pre-writing, drafting, getting feedback, revising and editing for publication. We recognised truth in the statement that “good writing is re-writing.” Fast-forward to our present and this wisdom seems to have been squashed by the daily mountain of other tasks every teacher confronts. Reading and grading the stack of required tasks in a curriculum is burdensome enough; who would ask for more? Thus, how many students at almost any level of schooling engage in regular cycles of drafting, feedback, revision, feedback and polishing? It’s safe to say, “probably not as many as we’d like,” knowing that such approaches not only develop better writing, but, in fact, can develop writers.
Teachers do what only Teachers Can
I suggest that removing some of the burden of the writing process as well as providing rich analytics and resources related to each teacher’s students is where technology can help. The fact that software can’t help developing writers craft ironic, poetic or poignant prose, doesn’t mean that it can’t help them with word choice, the mechanics of sentences or more sophisticated paragraphing and text structures. The way I see it, software can help students take ownership of their writing to the extent that when they submit their work to teachers, it represents their best efforts and warrants critical assessment. Again:
Let Software Do…
What Software can, so…
Teachers do what only Teachers Can