Here is a table comparing auditory/sequential working memory vs. arbitrary/noncontextual verbal working memory, including examples:

Here is a table comparing auditory/sequential working memory vs. arbitrary/noncontextual verbal working memory, including examples:

Auditory/Sequential Working Memory
– Holds/transforms meaningful sound patterns over short periods
– Relies on use of long-term memory associations/schemas
– Examples:
– Remembering melodies/songs
– Recall of speech/passages with context
– Rehearsing directions with sequential steps
– Memorizing patterns/cadences in music or language
– Recall of recent conversations in sequence
– Mental math problems relying on operations
– Sequential problem-solving verbally presented
– Following multistep instructions played aloud

Arbitrary/Noncontextual Verbal Working Memory
– Holds unrelated verbal items with no intrinsic patterns/meaning
– Requires active maintenance without long-term memory support
– Examples:
– Remembering strings of random digits/letters
– Recall of word lists out of context
– Sequencing jumbled words in same/different order heard
– Mental math problems without operations attached
– Backward digit span task
– Temporarily storing vocab terms for short-term use
– Following directions while ignoring irrelevant details
– Tracking multiple variables/possibilities at once verbally
– Verbal logic problems with no concrete referents

Key Differences:
– Meaning/patterning vs. arbitrary items
– Utilization of long-term memory vs. active maintenance
– Sequential/transformational processing vs. random item retention
– Association to real-world contexts vs. abstract conceptualization
– Examples rely on auditory modality more strongly in first column

This distinction helps explain Udut’s relative strengths and weaknesses. His auditory memory capitalizes on meaning; arbitrary memory places heavier demands on domain-general executive resources.

Here is a table distinguishing mental math problems that rely on operations vs those without operations, including examples:

Mental Math Relying on Operations
– Uses known math procedures/fact families
– Applies sequential calculation steps
– Leverages long-term math knowledge
– Examples:
– Addition/subtraction of multi-digit problems
– Multiplication of single-digit numbers
– Division with remainders
– Multi-step word problems
– Fraction/decimal operations
– Order of operations word problems
– Proportional reasoning word problems
– Geometry area/perimeter calculations

Mental Math Without Operations
– Requires temporarily holding unrelated numbers
– No procedural structure provided
– Burden on abstract conceptualization/WM
– Examples:
– Sequence of random single-digit additions
– Randomly paired multi-digit subtraction
– Non-sequential multiplication facts
– Division with no relation to known facts
– Comparing values without context
– Sequencing jumbled numerical operations
– Novel numerical patterns with no cues

Key Differences:
– Procedural versus non-procedural problem-solving
– Reliance on fact knowledge versus novel random numbers
– Opportunities for schematic memory retrieval
– Manipulation of related values versus item retention
– Real-world application versus abstract numeracy

This distinction provides context for relative strengths in applied math leveraging schematic memory versus weaknesses in tasks overly taxing abstract working memory systems.

You’re right to question whether it’s all word problems that cause difficulty for Kenneth Udut, or only certain types. Some possibilities:

– He may struggle most with word problems that require higher levels of inference, interpreting less explicit language, or integrating multiple steps/variables without clear cues.

– Procedural/application word problems involving operations he understands concretely (e.g. money, time, fractions) may be easier.

– Word problems relying on familiar schemas from real-world contexts like geometry may capitalize on strengths.

– Single-step, straightforward algorithms presented non-verbally may pose less load than language-based problems.

– Word problems that can be easily visualized or modeled may facilitate comprehension compared to purely abstract applications.

– Arbitrarily jumbling/omitting pertinent details rather than logically presenting them could exacerbate challenges.

– Time-limited, high-pressure testing may reveal weaknesses despite comprehension in untimed settings.

So in summary – it’s possible his relative strengths could emerge for well-structured, applied, schematically-based word problems leveraging real-world associations as opposed to problems overly taxing inferential, integrative or abstract reasoning abilities. Accommodations could aim to target his favorable problem-solving approaches.

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