"Learning Analytics" for L&D Professionals (Accredited)

Explore diagnostic analytics and A/B testing to validate relationships between training factors and overall satisfaction, using hypothesis testing, data visuals, and risk-aware decision making.

Evaluate the risks in A/B testing for learning analytics, detailing alpha (type I) and beta (type II) errors, confidence levels, and how tampering decisions can mislead outcomes.

Perform verbatim analytics with natural language processing to extract sentiments and representative words from participant feedback. Use word clouds and sentiment buckets in Orange for quick insights.

Learn natural language processing concepts and sentiment analysis basics. Apply data processing steps: corpus, lexicon, transformation, tokenization, stopword removal, and normalization (stemming/lemmatization) to enable effective sentiment analysis on data.

Learn to perform NLP in Orange, including installing the text mining addon, building a corpus, creating a word cloud, preprocessing text with tokenization and stop-word filtering, and applying sentiment analysis.

Apply behavioral analytics, aligned with Kirkpatrick's evaluation, to assess pre- and post-intervention changes in six account manager attributes—availability, responsiveness, professionalism, knowledge of client business, activeness, and contact frequency—via customer feedback.

Learn to choose the correct hypothesis test by identifying whether data are discrete or continuous and applying tests such as chi-square, correlation, regression, t-test, ANOVA, and logistic regression.

Explore knowledge evaluation analytics by comparing pre and post exam data to measure training impact. Use two-sample and paired tests, examine p-values, and infer statistically significant improvements.

Set up experiments for learning analytics by defining key performance indicators (kpis), identifying confounding factors and biases, and comparing experimental and control groups with before–after data to isolate effects.

Compare control and experimental groups using box plots and p-values to assess pre- and post-intervention effects, validate experimental design, and interpret revenue impact of the ALINDA intervention.

Learn how HR analytics links talent data to business performance and people experience through real-world case studies, including Google, Starbucks, Best Buy, AT&T, and IBM.

Explore how HR analytics links intangibles to business value, enabling data-driven decisions in recruitment, learning, and attrition management through predictive models and forecasting.

Explore how HR analytics maturity levels within a talent analytics maturity model enable data-driven decision making and strategic workforce insights.