Reading for Understanding Research Initiative

• Institute of Education Sciences– US Department of Education

• 5 or 6 “R&D Core Teams”• 1 or 2 “Network Assessment Teams”

• “Manhattan Project” / “Apollo Mission”– to improve (the teaching of)

reading for understanding/reading comprehension– $20 million over 5 years

A Center for Reading for Understanding

• A Research and Development Core Team to Integrate Vocabulary, Writing, Reasoning, Multimodal Literacies, and Oral Discourse to Improve Reading Comprehension

• Principal location: UB• Satellite locations: Niagara University,

Penn State University• Affiliated school districts: Niagara Falls CSD,

Cleveland Hill USD,State College Area SD

Projects• Writing Intensive Reading Comprehension

– Jim Collins (UB/LAI)

• Contextual Vocabulary Acquisition– Bill Rapaport (UB/CSE & CCS)

• Multimodal Literacies– Kathleen Collins (Penn State)

• Virtual Interactive Environments to improve vocabulary and reading comprehension – Lynn Shanahan & Mary McVee (UB/LAI)

• Interactively Modeled Metacognitive Thinking – Rob Erwin (Niagara U)

• Bilingualism and Basic Cognitive Processes– Janina Brutt-Griffler (UB/LAI)

o Experimental Design & Statistical Analysis– Ariel Aloe (UB/CSEP)

Writing-Intensive Reading Comprehension— Jim Collins (LAI)

• Based on previous successful research• “Thinksheets” that guide students’ writing about a

text improve their reading comprehension– Thinksheets begin with questions whose answers can be

found directly in the text– Then gradually ask more abstract questions requiring

inference.

• Goals: – extend this research– apply to more grade levels– semi-automate development of thinksheets

Multimodal Literacies— Kathleen Collins (Penn State)

• Previous research:– if teachers work with professional artists to develop strategies for

integrating the arts as forms of inquiry and meaning-making into the school curriculum, then more students are able to engage successfully with core curricula.

• Multimodal interventions & enhancements will be designed to include the following broad categories:– Visual/Graphic (still photography, illustration, digital video)– Oral/Interactional (scripted dialogue, interviews,

guided conversations, podcasts)– Tactile (movement, model building, sculpting)

Virtual Interactive Environments to Develop Vocabulary Knowledge— Lynn Shanahan & Mary McVee (LAI)

• Develop extensible & effective computational framework for improving vocabulary knowledge through virtual-contextual learning

• Couple a virtual world, large projection screen, stereo sound, & positional tracking using commodity devices (e.g., Wii controllers).

• Virtual environment will be created using a programming toolkit developed for use in computer games and engineering simulations. – NYSCEDII

• Rather than interact with a computer monitor, the environment will use projection technology to provide learners with the opportunity to interact with objects of actual size, increasing the immersion into the virtual world.

Interactively Modeled Metacognitive Thinking— Rob Erwin (NU)

• Instead of teaching specific strategies for reading comprehension…– (which have recently been shown to be worse

than teaching textual content)

• … Have students develop their own strategies on an as-needed basis

• Then compare to strategies- & content-approaches

Bilingualism & Basic Cognitive Processes— Janina Brutt-Griffler (LAI)

• What are the cognitive advantages of being a bilingual reader?

• What are the cognitive aspects of text comprehension and possible linguistic advantages observed in bilingual readers?

Contextual Vocabulary Acquisition— Bill Rapaport (CSE & CCS)

1. Previous research:• A reader's understanding of a word's meaning in a context

is a function of both the context (the surrounding words) and the reader's prior knowledge.

2. Already accomplished:a) A procedure for successful CVA can be expressed in terms so precise that

they can be programmed into a computer.b) That computational procedure can then be converted into a strategy

teachable to human readers

3. We propose to embed this procedure in a curricular intervention that can help readers improve both

a) their vocabulary andb) their reading comprehension.

CVA (cont’d)

• Our goal is not to improve vocabulary per se, but:– to improve reading comprehension by

• active thinking about the text

• with a specific goal of vocabulary enrichment in mind

– and to provide readers with a method that can be used independently…

• e.g., when they are reading on their own

…to learn new vocabulary and to improve comprehension.

CVA (cont’d)

• GOFAI:– if we know how to explicitly teach some

cognitive task to humans• e.g., play chess, do calculus, prove theorems

– then we can explicitly program a computer to do that task pretty much as humans do it.

• Our CVA algorithms fall into this category Can do the converse!– I.e., can have a human reader simulate our program

CVA (cont’d)

• Not “think like a computer”– I.e., rigidly, mechanically, uncreatively

• But:– what we have learned by teaching a computer to do CVA

can now help us teach human readers who need guidance in CVA.

“Contextual Semantic Investigation” (CSI):A Curriculum Outline

1. Teacher models CSI

2. Teacher models CSI with student participation

3. Students model CSI with teacher assistance

4. Students do CSI in small groups

5. Students do CSI on their own

CSI: Algorithms (for Humans)

I. Become aware of word X& of need to understand X

II. Repeat:

A. Generate hypothesis H about X’s meaningB. Test H until H is a plausible meaning for X

in the current “wide” context

IIB. Test H

1. Replace all occurrences of X in sentence by H

2. If Sentence (X := H) makes sense then proceed with reading else generate new H

IIA. Generate H

1. Do in any order to generate H:a) Make an “intuitive” guess

b) Try to recall X’s meaning from previous reading

c) Use X’s morphology

d) Make an “educated” guess

IIA1d: Make an “Educated Guess”

i. Re-read X’s sentence slowly & actively

ii. Determine X’s part of speech

iii. Summarize entire text so far

iv. Activate your PK about the topic

v. Make inferences from text + PK

vi. Generate H based on all this

IIA2: If all previous steps fail,then do CVA

a) “Solve for X”

b) Search context for clues

c) Create H

IIA2a: Solve for X

i. Syntactically manipulate X’s sentenceso that X is the subject

ii. Generate a list of possible synonyms(as “hypotheses in waiting”)

IIA2aii: Generate a list of “hypotheses in waiting”

• “Sandra had won the dance contest & the audience’s cheers brought her to the stage for an encore. ‘Every step she takes is so perfect & graceful,’ Ginny said grudgingly, as she watched Sandra dance.” (Beck, McKeown, McCaslin 1983)– A “misdirective” context?– But: syntactic manipulation yields:

• Grudgingly is a way of:– saying something (e.g., quickly, loudly,…)– praising someone’s performance (lavishly, honestly…)– apparently praising

(e.g., ironically, sarcastically, reluctantly…)

IIA2b: Search context for clues

i. If X is a noun, then search context for clues about X’s…

• class membership• properties• structure• acts• agents• comparisons• contrasts

IIA2b: Search context for cluesii. If X is a verb,

then search context for clues about X’s…

• class membership what kind of act Xing is what kinds of acts are Xings

• properties of Xing (e.g., manner)

• transitivity look for agents and objects of Xing

• cause & effect information

• comparisons & contrasts

IIA2b: Search context for clues

iii. If X is an adjective or adverb, then search context for clues about X’s…

• class membership is it a color adjective, a size adjective, a shape adjective, etc.?

• contrasts is it an opposite or complement of something else mentioned?

• parallels is it one of several otherwise similar modifiers in the sentence?

IIA2c: Create H

• Aristotelian definitions:– What kind of thing is X?– How does it differ from other things of that kind?

• Schwartz & Raphael definition “map”– What is X?– What is it like?– What are some examples?

• Express (“important” parts of) definition framein a single sentence– Cf. Collins COBUILD

CVA (cont’d)

• Research questions:1. Can a computer algorithm be translated into a

successful curricular intervention?a) Does this computer-based curriculum improve

(meaning) vocabulary?i. CVA students vs. “typical context-based” students

vs. “no treatment” control

– Each read same passage with single unfamiliar word& figure out a meaning from context

ii. CVA students vs. “direct-method” control at time t

– Tested on meaning at time t > t

CVA (cont’d)

1. Can a computer algorithm be translated into a successful curricular intervention?

b) Does the algorithm-based curriculum improve reading comprehension?• CVA students vs. “typical context-based” students

vs. no-treatment students

• Test for reading comprehension on passages…– containing unfamiliar words

– with no unfamiliar word

CVA (cont’d)

2. Other issues:• “teacher training”/professional development

• oral discussion:– explicit instruction on using language to reason is valuable

• use thinksheet as “detective”/“scientist” notebook

• role & nature of prior knowledge– what is needed, how to identify it, how to elicit it, etc.

• test at different ages & in STEM vs. ELA

• develop software for use in classroom– student can ask Cassie how she figured it out