Effectiveness of spaced learning when the data have personal meaning

Question

There is much study of spaced learning (expanded retrieval, spaced retrieval, spaced repetition system, gradual-interval recall, etc), and much of the discussion is about around the optimal distribution of the recall over time (often in comparison to equally spaced recall events or mass recall).

Instead of a focus on time, have studies considered the nature of the data to be remembered?

1) Have studies considered autobiographical data in comparison to non-autobiographical?

So, for example, if the data to be remembered are personal and relate to, say, family and friends, then will the use of spaced learning be more effective than, say, learning new vocabulary? I am thinking about people with Alzheimer's or other diseases that impact memory.

Put another way: is spaced learning more effective when applied to personal facts vs. impersonal facts? Or does it make no difference what the data are?

2) Is there any impact if the data are all related in some way? For example, suppose the task is to learn vocabulary for parts of system (e.g. family, parent, child, sibling, etc or the parts of an automobile, ...) in comparison to a set of unrelated words. Does it make a difference if the set of concepts to be remembered have associations which the mind can use to link them together?

Answer 1

1. I'm not sure if personal relevance of data has been studied directly, but this factor seems related to depth of processing ^{[Wikipedia link A]}, which improves memory encoding. I'm not aware of any research on an interaction between depth of processing ^{[Wikipedia link B]} and whether learning is spaced (as an experimentally controlled variable). I hope others will have more info for you on these questions.
2. Relatedness of data has a clear effect on learning and recall. Chunking is a mnemonic technique that capitalizes on relationships among data to facilitate info-reconstructive processes in memory. This can even be performed on ostensibly unrelated data using elaborative rehearsal. ^{(See also depth of processing link A.)} Example methods include combining disparate elements in a narrative, or associating them with landmarks using the method of loci.