The art of goal setting

Ken Beatty
A business woman looking and pointing at a wall full of post it notes

Reading time: 4 minutes

Dr. Ken Beatty defines goals and explains why we should think of them as doors to open rather than fixed targets.

Goals as doors

My eldest son, Nathan, failed to achieve the biggest goal of his life: becoming a garbage truck driver. It's hardly surprising - he was only four years old at the time. His ambition likely dissolved once he realized that garbage trucks sometimes smell bad. Before then, he'd mostly observed them from the safety of our apartment window.

As is the case with most people, his goals have changed. Completing his degree in international economics, hoping to work in technology startups until he forms one himself. Or maybe not. Goals evolve.

Researchers and teachers have known for decades that goals are vitally important motivations in general education and language learning. After examining 800+ studies, Hattie (2009) identified goals as among the most powerful instructional interventions for improving student success.

The basic message is that goals are good. However, other researchers (Rowe, Mazzotti, Ingram, & Lee, 2017) suggest that teachers have trouble embedding them in lessons.

Part of the problem might be in finding a way to visualize goals. Goals are often pictured as archery targets or soccer nets, but a more useful metaphor is a door. When we have a goal, we may not fully understand it until we enter into the goal, as if it were a room, inevitably finding choices of other doors leading off in other directions.

Understanding where goals come from

Before we start to set goals for our students, it's important that we have a degree of self-awareness and understand where our own attitudes and ideas come from.

As teachers, we tend to resemble the people who inspired us most. Our own teachers, good and bad, shape our attitudes toward teaching and language-learning goals.

Who was your favorite teacher? In my case, my all-time favorite teacher was Mr. Chiga, who, in 1970, taught me Grade 7 and was about to retire. He was a Renaissance man. Short and tough with fingers like cigars, he would occasionally lead us from the playground up two flights of stairs to our classroom… walking on his hands. Yet these same hands were delicate enough for his hobby of making violins, a fact I only learned later, because, unlike me, Mr. Chiga was modest.

Mr. Chiga loved literature and taught us Greek and Roman history with a sense of joy that has never left me. One would think that his educational goals would be a perfect foundation for my own. Perhaps. But a quick check on the timeline shows that if he was about to retire in 1970, he was probably born in 1905 and likely graduated from teachers' college around 1925.

It's ironic that although my Ph.D. is in the area of computer-assisted language learning, my favorite teacher began his career two years before the invention of the television, and, moreover, all his teachers would have been born in the 1800s.

It's a long story to make a short point: as teachers, we need to reflect on where our teaching and learning goals come from and question them. We also need to avoid those things that our least favorite teachers did.

Setting goals

Are the goals we set for our students sometimes too low? Undoubtedly.

As a Grade 11 student, my only ambition in life was to take a two-year photo technician course. My counselor discouraged me, saying I wasn't academic enough and suggested a job at the wood mill instead. In a sense, he closed a door.

I switched schools where another favorite teacher, Mr. Ferguson, patiently kept me after school for six weeks, teaching me how to write essays and, by extension, how to think. He dangled the motivation of a university education before me and set me on my path there. And that was a door opened.

So what's the lesson here? More than just knowing where goals come from, we also need to be aware of the power of goal setting and how it can drastically alter a particular student's life trajectory.

Closing doors, rather than opening them, often stunts growth and limits possibilities. It can even lead to students forming life-long assumptions about themselves that just aren't true - "I'm no good at math," "I'm not cut out for independent travel", etc. Opening doors, however, can bring our students entirely new perspectives on life.

Expecting goals to change

When it comes to changing goals, there are a number of factors to take into account, including forming a better sense of self. We might start off with many ambitions but we measure ourselves against the realities of our skill sets and modify our goals.

For example, a student who experiences a lot of success in learning English is more likely to consider careers that require it. Teachers, too, are more likely to offer direction: "You write very well. Have you considered a career in journalism?"

Today, countless jobs require a second language or provide better promotion opportunities for students who speak two or more languages. Yet, students oriented toward employment opportunities may have difficulty understanding the long-term advantages of learning a second language if specific jobs are not on their radar.

This leads to two questions:

  • What goals should we help students set for themselves?
  • And how should teachers suggest them?

Many goals are based on the educational standards that govern our profession. The Global Scale of English (GSE), in particular, is helpful to both textbook writers and teachers in identifying language goals and provides teachers with detailed steps to achieve them.

But beyond such standards are those two magic ingredients that teachers share with language learners: joy and motivation.

Teachers spread joy in learning by example, making language learning engaging and pleasurable. Teachers also motivate students by helping them identify personal goals, giving them reasons why language proficiency is not just worthwhile in general but is perhaps one key to future success.

It might even lead to a job driving a garbage truck.

Read more about language teaching

References

Hattie, J. A. (2009).ÌýVisible Learning: A Synthesis Of Over 800 Meta-Analyses Relating To Achievement.ÌýNew York: Routledge

Rowe, D.A, Mazzotti, V.L., Ingram, A., & Lee, S. (2017). Effects of Goal-Setting Instruction on Academic Engagement for Students At Risk.ÌýCareer Development and Transition for Exceptional Individuals.Ìý40(1) 25–35.

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    Can computers really mark exams? Benefits of ELT automated assessments

    By ÃÛÌÒapp Languages

    Automated assessment, including the use of Artificial Intelligence (AI), is one of the latest education tech solutions. It speeds up exam marking times, removes human biases, and is as accurate and at least as reliable as human examiners. As innovations go, this one is a real game-changer for teachers and students. 

    However, it has understandably been met with many questions and sometimes skepticism in the ELT community – can computers really mark speaking and writing exams accurately? 

    The answer is a resounding yes. Students from all parts of the world already take AI-graded tests.  a²Ô»å VersantÌýtests – for example – provide unbiased, fair and fast automated scoring for speaking and writing exams – irrespective of where the test takers live, or what their accent or gender is. 

    This article will explain the main processes involved in AI automated scoring and make the point that AI technologies are built on the foundations of consistent expert human judgments. So, let’s clear up the confusion around automated scoring and AI and look into how it can help teachers and students alike. 

    AI versus traditional automated scoring

    First of all, let’s distinguish between traditional automated scoring and AI. When we talk about automated scoring, generally, we mean scoring items that are either multiple-choice or cloze items. You may have to reorder sentences, choose from a drop-down list, insert a missing word- that sort of thing. These question types are designed to test particular skills and automated scoring ensures that they can be marked quickly and accurately every time.

    While automatically scored items like these can be used to assess receptive skills such as listening and reading comprehension, they cannot mark the productive skills of writing and speaking. Every student's response in writing and speaking items will be different, so how can computers mark them?

    This is where AI comes in. 

    We hear a lot about how AI is increasingly being used in areas where there is a need to deal with large amounts of unstructured data, effectively and 100% accurately – like in medical diagnostics, for example. In language testing, AI uses specialized computer software to grade written and oral tests. 

    How AI is used to score speaking exams

    The first step is to build an acoustic model for each language that can recognize speech and convert it into waveforms and text. While this technology used to be very unusual, most of our smartphones can do this now. 

    These acoustic models are then trained to score every single prompt or item on a test. We do this by using human expert raters to score the items first, using double marking. They score hundreds of oral responses for each item, and these ‘Standards’ are then used to train the engine. 

    Next, we validate the trained engine by feeding in many more human-marked items, and check that the machine scores are very highly correlated to the human scores. If this doesn’t happen for any item, we remove it, as it must match the standard set by human markers. We expect a correlation of between .95-.99. That means that tests will be marked between 95-99% exactly the same as human-marked samples. 

    This is incredibly high compared to the reliability of human-marked speaking tests. In essence, we use a group of highly expert human raters to train the AI engine, and then their standard is replicated time after time.  

    How AI is used to score writing exams

    Our AI writing scoring uses a technology called . LSA is a natural language processing technique that can analyze and score writing, based on the meaning behind words – and not just their superficial characteristics. 

    Similarly to our speech recognition acoustic models, we first establish a language-specific text recognition model. We feed a large amount of text into the system, and LSA uses artificial intelligence to learn the patterns of how words relate to each other and are used in, for example, the English language. 

    Once the language model has been established, we train the engine to score every written item on a test. As in speaking items, we do this by using human expert raters to score the items first, using double marking. They score many hundreds of written responses for each item, and these ‘Standards’ are then used to train the engine. We then validate the trained engine by feeding in many more human-marked items, and check that the machine scores are very highly correlated to the human scores. 

    The benchmark is always the expert human scores. If our AI system doesn’t closely match the scores given by human markers, we remove the item, as it is essential to match the standard set by human markers.

    AI’s ability to mark multiple traits 

    One of the challenges human markers face in scoring speaking and written items is assessing many traits on a single item. For example, when assessing and scoring speaking, they may need to give separate scores for content, fluency and pronunciation. 

    In written responses, markers may need to score a piece of writing for vocabulary, style and grammar. Effectively, they may need to mark every single item at least three times, maybe more. However, once we have trained the AI systems on every trait score in speaking and writing, they can then mark items on any number of traits instantaneously – and without error. 

    AI’s lack of bias

    A fundamental premise for any test is that no advantage or disadvantage should be given to any candidate. In other words, there should be no positive or negative bias. This can be very difficult to achieve in human-marked speaking and written assessments. In fact, candidates often feel they may have received a different score if someone else had heard them or read their work.

    Our AI systems eradicate the issue of bias. This is done by ensuring our speaking and writing AI systems are trained on an extensive range of human accents and writing types. 

    We don’t want perfect native-speaking accents or writing styles to train our engines. We use representative non-native samples from across the world. When we initially set up our AI systems for speaking and writing scoring, we trialed our items and trained our engines using millions of student responses. We continue to do this now as new items are developed.

    The benefits of AI automated assessment

    There is nothing wrong with hand-marking homework tests and exams. In fact, it is essential for teachers to get to know their students and provide personal feedback and advice. However, manually correcting hundreds of tests, daily or weekly, can be repetitive, time-consuming, not always reliable and takes time away from working alongside students in the classroom. The use of AI in formative and summative assessments can increase assessed practice time for students and reduce the marking load for teachers.

    Language learning takes time, lots of time to progress to high levels of proficiency. The blended use of AI can:

    • address the increasing importance of formative assessmentÌýto drive personalized learning and diagnostic assessment feedback 

    • allow students to practice and get instant feedback inside and outside of allocated teaching time

    • address the issue of teacher workload

    • create a virtuous combination between humans and machines, taking advantage of what humans do best and what machines do best. 

    • provide fair, fast and unbiased summative assessment scores in high-stakes testing.

    We hope this article has answered a few burning questions about how AI is used to assess speaking and writing in our language tests. An interesting quote from Fei-Fei Li, Chief scientist at Google and Stanford Professor describes AI like this:

    “I often tell my students not to be misled by the name ‘artificial intelligence’ — there is nothing artificial about it; A.I. is made by humans, intended to behave [like] humans and, ultimately, to impact human lives and human society.â€

    AI in formative and summative assessments will never replace the role of teachers. AI will support teachers, provide endless opportunities for students to improve, and provide a solution to slow, unreliable and often unfair high-stakes assessments.

    Examples of AI assessments in ELT

    At ÃÛÌÒapp, we have developed a range of assessments using AI technology.

    Versant

    The Versant tests are a great tool to help establish language proficiency benchmarks in any school, organization or business. They are specifically designed for placement tests to determine the appropriate level for the learner.

    PTE Academic

    The  is aimed at those who need to prove their level of English for a university place, a job or a visa. It uses AI to score tests and results are available within five days. 

    ÃÛÌÒapp English International Certificate (PEIC)

    ÃÛÌÒapp English International Certificate (PEIC) also uses automated assessment technology. With a two-hour test available on-demand to take at home or at school (or at a secure test center). Using a combination of advanced speech recognition and exam grading technology and the expertise of professional ELT exam markers worldwide, our patented software can measure English language ability.