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:
• Satellite locations:
UB
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 meaningmaking 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 virtualcontextual 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- & contentapproaches
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:
•
2.
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.
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 and
b) 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.
2.
3.
4.
5.
Teacher models CSI
Teacher models CSI with student participation
Students model CSI with teacher assistance
Students do CSI in small groups
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 meaning
B. 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)
b)
c)
d)
Make an “intuitive” guess
Try to recall X’s meaning from previous reading
Use X’s morphology
Make an “educated” guess
IIA1d: Make an “Educated Guess”
i.
ii.
iii.
iv.
v.
vi.
Re-read X’s sentence slowly & actively
Determine X’s part of speech
Summarize entire text so far
Activate your PK about the topic
Make inferences from text + PK
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 sentence
so 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 clues
ii.
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 frame
in 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?
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
i.
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:
–
•
•
use thinksheet as “detective”/“scientist” notebook
role & nature of prior knowledge
–
•
•
explicit instruction on using language to reason is valuable
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