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Theories of ReadingFrom Static Text to Living Stories: How Reading Learned to Talk
From Static Text to Living Stories: How Reading Learned to Talk
By Scott Crossley, iTELL Chief Innovation Officer
When I was three, the family television broke, and my parents decided not to replace it. After that, all media came through the radio or through print. Luckily, there were a lot of books in my house, and, at some point, I remember looking at a page and words suddenly appearing. There were just a few at first, but then they started to multiply quickly, and my life was suddenly consumed by reading.
This had a few positive outcomes. First, while far from a star at school, I excelled at reading, so much so that teachers questioned my parents about self-reports for the number of books I had read over the summer. When it came to reading, I was a star. Second, I found a connection with my father, spending innumerable number of nights at the city library with him. Last, as the youngest child (by a long stretch) and as a child whose parents firmly believed in the law of grounding, I spent a lot of time alone. It was at this time that books not only became a source of distraction and entertainment but also companionship because the books started talking to me. The main characters would come alive in my head and conversations and plots well beyond the printed word ensued.
Later, when studying for my Ph.D. and taking a course on reading theory, I learned that this was natural and, was in a fact, a very good comprehension strategy. Good readers do not treat texts as static entities devoid of character and emotion. Instead, good readers interact with the texts they read. This might be as simple as predicting what will happen next in a text, elaborating on a plot, argument, or idea, or summarizing a text as it is read. The best reading strategies, though, revolve around inferencing and involve readers making logical inferences given within the text, between segments of texts, or between the text and their background knowledge. Creating such inferences is known to improve text comprehension because readers are actively generating a mental representation of a text to include the where, when, what, and how events unfold and who is involved.
Helping readers generate inferences is one of the main goals of iTELL. iTELL does this by asking short questions that often require readers to make inferences and by assessing readers’ summarization of text, which often leads to natural inferences. iTELL also helps readers generate inferences through a dialogic chatbot called Strategic Thinking And Interactive Reading Support (STAIRS). STAIRS guides the reader through a self-explanation of a text chunk through conversational questions designed to help the reader interact with the text. The STAIRS chatbot asks users to reread a section of text and elaborate on that text, predict what will happen next in that text, or make inferences or logical connections between segments of that text and/or a reader’s background knowledge.
This is all done implicitly in iTELL because strong reading strategies occur naturally. Additionally, we want iTELL users to focus on the reading demands of the text and not strategy development. However, with experience and practice, strategies like inference generation can develop intuitively and reading skills can develop naturally. Our hope is that iTELL helps texts become alive for all readers, leading to a more engaging and rich learning experience. We know that texts can be companions, and iTELL was developed to foster that relationship.
By Scott Crossley, iTELL Chief Innovation Officer
When I was three, the family television broke, and my parents decided not to replace it. After that, all media came through the radio or through print. Luckily, there were a lot of books in my house, and, at some point, I remember looking at a page and words suddenly appearing. There were just a few at first, but then they started to multiply quickly, and my life was suddenly consumed by reading.
This had a few positive outcomes. First, while far from a star at school, I excelled at reading, so much so that teachers questioned my parents about self-reports for the number of books I had read over the summer. When it came to reading, I was a star. Second, I found a connection with my father, spending innumerable number of nights at the city library with him. Last, as the youngest child (by a long stretch) and as a child whose parents firmly believed in the law of grounding, I spent a lot of time alone. It was at this time that books not only became a source of distraction and entertainment but also companionship because the books started talking to me. The main characters would come alive in my head and conversations and plots well beyond the printed word ensued.
Later, when studying for my Ph.D. and taking a course on reading theory, I learned that this was natural and, was in a fact, a very good comprehension strategy. Good readers do not treat texts as static entities devoid of character and emotion. Instead, good readers interact with the texts they read. This might be as simple as predicting what will happen next in a text, elaborating on a plot, argument, or idea, or summarizing a text as it is read. The best reading strategies, though, revolve around inferencing and involve readers making logical inferences given within the text, between segments of texts, or between the text and their background knowledge. Creating such inferences is known to improve text comprehension because readers are actively generating a mental representation of a text to include the where, when, what, and how events unfold and who is involved.
Helping readers generate inferences is one of the main goals of iTELL. iTELL does this by asking short questions that often require readers to make inferences and by assessing readers’ summarization of text, which often leads to natural inferences. iTELL also helps readers generate inferences through a dialogic chatbot called Strategic Thinking And Interactive Reading Support (STAIRS). STAIRS guides the reader through a self-explanation of a text chunk through conversational questions designed to help the reader interact with the text. The STAIRS chatbot asks users to reread a section of text and elaborate on that text, predict what will happen next in that text, or make inferences or logical connections between segments of that text and/or a reader’s background knowledge.
This is all done implicitly in iTELL because strong reading strategies occur naturally. Additionally, we want iTELL users to focus on the reading demands of the text and not strategy development. However, with experience and practice, strategies like inference generation can develop intuitively and reading skills can develop naturally. Our hope is that iTELL helps texts become alive for all readers, leading to a more engaging and rich learning experience. We know that texts can be companions, and iTELL was developed to foster that relationship.